Mapping and Plotting
davegoblue
3/10/2020
Background
This document is to provide some plotting examples for reference.
Example #1: US State and County Maps
The package usmap contains maps of US states and counties. There is also some associated data available about state and county demographics.
Example code includes:
library(tidyverse)## -- Attaching packages ------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.2.1 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.4
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts ---------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# Population by county
data(countypop, package="usmap")
# Population by state
data(statepop, package="usmap")
# Poverty rate by county
data(countypov, package="usmap")
# Poverty rate by state
data(statepov, package="usmap")
# Population of largest city by state
data(citypop, package="usmap")
# Location of earthquakes
data(earthquakes, package="usmap")
# Included datasets
countypop## # A tibble: 3,142 x 4
## fips abbr county pop_2015
## <chr> <chr> <chr> <dbl>
## 1 01001 AL Autauga County 55347
## 2 01003 AL Baldwin County 203709
## 3 01005 AL Barbour County 26489
## 4 01007 AL Bibb County 22583
## 5 01009 AL Blount County 57673
## 6 01011 AL Bullock County 10696
## 7 01013 AL Butler County 20154
## 8 01015 AL Calhoun County 115620
## 9 01017 AL Chambers County 34123
## 10 01019 AL Cherokee County 25859
## # ... with 3,132 more rowsstatepop## # A tibble: 51 x 4
## fips abbr full pop_2015
## <chr> <chr> <chr> <dbl>
## 1 01 AL Alabama 4858979
## 2 02 AK Alaska 738432
## 3 04 AZ Arizona 6828065
## 4 05 AR Arkansas 2978204
## 5 06 CA California 39144818
## 6 08 CO Colorado 5456574
## 7 09 CT Connecticut 3590886
## 8 10 DE Delaware 945934
## 9 11 DC District of Columbia 672228
## 10 12 FL Florida 20271272
## # ... with 41 more rowscountypov## # A tibble: 3,142 x 4
## fips abbr county pct_pov_2014
## <chr> <chr> <chr> <dbl>
## 1 01001 AL Autauga County 13.1
## 2 01003 AL Baldwin County 13
## 3 01005 AL Barbour County 25.4
## 4 01007 AL Bibb County 18.1
## 5 01009 AL Blount County 17.5
## 6 01011 AL Bullock County 35.1
## 7 01013 AL Butler County 25
## 8 01015 AL Calhoun County 20.5
## 9 01017 AL Chambers County 21.3
## 10 01019 AL Cherokee County 18.6
## # ... with 3,132 more rowsstatepov## # A tibble: 51 x 4
## fips abbr full pct_pov_2014
## <chr> <chr> <chr> <dbl>
## 1 01 AL Alabama 19.2
## 2 02 AK Alaska 11.4
## 3 04 AZ Arizona 18.2
## 4 05 AR Arkansas 18.7
## 5 06 CA California 16.4
## 6 08 CO Colorado 12.1
## 7 09 CT Connecticut 10.8
## 8 10 DE Delaware 13
## 9 11 DC District of Columbia 18.4
## 10 12 FL Florida 16.6
## # ... with 41 more rowscitypop## # A tibble: 51 x 6
## lon lat state abbr most_populous_city city_pop
## <dbl> <dbl> <chr> <chr> <chr> <dbl>
## 1 -86.8 33.6 Alabama AL Birmingham 212237
## 2 -150. 61.2 Alaska AK Anchorage 291826
## 3 -112. 33.4 Arizona AZ Phoenix 1445632
## 4 -92.3 34.7 Arkansas AR Little Rock 193524
## 5 -118. 34.0 California CA Los Angeles 3792621
## 6 -105. 39.8 Colorado CO Denver 600158
## 7 -73.2 41.2 Connecticut CT Bridgeport 144229
## 8 -75.6 39.8 Delaware DE Wilmington 70851
## 9 -77.0 38.9 District of Columbia DC Washington 693972
## 10 -81.7 30.3 Florida FL Jacksonville 880619
## # ... with 41 more rowsearthquakes## # A tibble: 2,254 x 3
## lon lat mag
## <dbl> <dbl> <dbl>
## 1 -118. 35.7 2.79
## 2 -118. 36.3 3.1
## 3 -118. 36.2 2.74
## 4 -125. 40.4 2.77
## 5 -118. 35.7 2.59
## 6 -118. 35.7 2.82
## 7 -118. 35.7 2.78
## 8 -124. 40.3 3.42
## 9 -124. 41.7 2.5
## 10 -119. 35.3 2.58
## # ... with 2,244 more rows# Basic, empty US maps
usmap::plot_usmap(regions="states")
usmap::plot_usmap(regions="counties")
# Basic, empty US maps subsetted to an area
usmap::plot_usmap(regions="states",
include=c("WA", "OR", "CA", "NV", "ID", "MT", "WY", "UT", "CO", "AZ", "NM")
)
usmap::plot_usmap(regions="counties", include=c("MN", "WI", "MI", "OH", "PA", "NY", "IN", "IL"))
# Basic, subsetted state map with poverty rates included
usmap::plot_usmap(regions="states",
include=c("WA", "OR", "CA", "NV", "ID", "MT", "WY", "UT", "CO", "AZ", "NM"),
values="pct_pov_2014", data=statepov, labels=TRUE
) +
scale_fill_continuous(low="lightblue", high="darkblue", "Poverty Rate (%)") +
labs(title="Poverty Rates by Western and Mountain States")
# Basic, subsetted county map with poverty rates included
usmap::plot_usmap(regions="counties", include=c("MN", "WI", "MI", "OH", "PA", "NY", "IN", "IL"),
values="pct_pov_2014", data=countypov, labels=FALSE
) +
scale_fill_continuous(low="lightblue", high="darkblue", "Poverty Rate (%)") +
labs(title="Poverty Rates by County in Great Lakes States")
Example #2: Converting and adding lat/lon data
The latitude and longitude data can be converted to a form suitable for usmap by using the usmap_transform function.
Example code includes:
# Transform the earthquakes data
trQuakes <- usmap::usmap_transform(earthquakes)
str(trQuakes)## 'data.frame': 2254 obs. of 5 variables:
## $ lon : num -118 -118 -118 -125 -118 ...
## $ lat : num 35.7 36.3 36.2 40.4 35.7 ...
## $ mag : num 2.79 3.1 2.74 2.77 2.59 2.82 2.78 3.42 2.5 2.58 ...
## $ lon.1: num -1575319 -1632293 -1595342 -2065325 -1574008 ...
## $ lat.1: num -872720 -785212 -811800 -197614 -867723 ...# Add as a layer to the state map
usmap::plot_usmap(regions="states") +
geom_point(data=trQuakes, aes(x=lon.1, y=lat.1, size=mag), alpha=0.4) +
labs(title="Earthquakes of Magnitude 2.5+ (H1 2019)")
# Transform the largest city data
trCity <- usmap::usmap_transform(citypop)
str(trCity)## 'data.frame': 51 obs. of 8 variables:
## $ lon : num -86.8 -112.1 -92.3 -118.2 -104.9 ...
## $ lat : num 33.6 33.5 34.7 34 39.8 ...
## $ state : chr "Alabama" "Arizona" "Arkansas" "California" ...
## $ abbr : chr "AL" "AZ" "AR" "CA" ...
## $ most_populous_city: chr "Birmingham" "Phoenix" "Little Rock" "Los Angeles" ...
## $ city_pop : num 212237 1445632 193524 3792621 600158 ...
## $ lon.1 : num 1220905 -1120927 702478 -1673156 -417275 ...
## $ lat.1 : num -1165156 -1202575 -1107534 -1034842 -570173 ...# Add as a layer to the state map
usmap::plot_usmap(regions="states") +
geom_point(data=trCity, aes(x=lon.1, y=lat.1, size=city_pop)) +
labs(title="Largest City by State")
Example #3: Filtering and coloring by region
The census region definitions are included, and can be used to filter or color the maps.
Example code includes:
# Filter the map to include only new_england, mid_atlantic, and south_atlantic
usmap::plot_usmap(regions="states",
include=c(usmap::.new_england, usmap::.mid_atlantic, usmap::.south_atlantic)
)
# Create regions data for US states
regionData <- usmap::statepop %>%
mutate(region=as.factor(ifelse(abbr %in% usmap::.midwest_region, 1, 0)))
usmap::plot_usmap(regions="states", data=regionData, values="region") +
scale_fill_discrete("Midwest") +
labs(title="Midwest Region US States")
# Enhanced Coloring and Labelling
usmap::plot_usmap(regions="states", data=regionData, values="region") +
scale_fill_manual(values=c("lightgray", "lightblue"), "", labels=c("Other", "Midwest")) +
labs(title="Midwest Region US States")
Example #4: Labelling geographies
Since usmap is built on ggplot2, the standard techniques from ggplot2 can be used to enhance the geography labelling. Further, centroids for the geographies are available in loadable files.
Example code includes:
# Base state map labelled with defaults
usmap::plot_usmap(regions="states", labels=TRUE, label_color="red")
# Base county map labelled with defaults
usmap::plot_usmap(regions="counties", include=c("TX", "OK"), labels=TRUE, label_color="grey")
# Load state centroid data
stCenter <- utils::read.csv(system.file("extdata",
paste0("us_", "states", "_centroids.csv"), package = "usmap"
),
colClasses = c(rep("numeric", 2), rep("character", 3)), stringsAsFactors = FALSE
)
# Load county centroid data
ctCenter <- utils::read.csv(system.file("extdata",
paste0("us_", "counties", "_centroids.csv"), package = "usmap"
),
colClasses = c(rep("numeric", 2), rep("character", 4)), stringsAsFactors = FALSE
)
# Add state labels using geom_text
regionData <- usmap::statepop %>%
mutate(region=as.factor(ifelse(abbr %in% usmap::.midwest_region, 1, 0))) %>%
left_join(stCenter %>% select(x, y, full, fips) %>% rename(fname=full)) %>%
mutate(fname=ifelse(fname=="District of Columbia", "DC", str_replace_all(fname, " ", "\n")))## Joining, by = "fips"usmap::plot_usmap(regions="states", data=regionData[, c("fips", "region")], values="region") +
scale_fill_manual(values=c("lightgray", "lightblue"), "", labels=c("Other", "Midwest")) +
labs(title="Midwest Region US States") +
geom_text(data=filter(regionData, region==1), aes(x=x, y=y, label=fname), size=2.5)
# Add county labels using geom_text
regionData <- usmap::countypop %>%
mutate(region=as.factor(case_when(abbr=="OK" ~ 1, abbr=="TX" ~ 2, TRUE ~ 0))) %>%
left_join(ctCenter %>% select(x, y, county, fips) %>% rename(cname=county)) %>%
mutate(cname=str_replace_all(str_replace(cname, " County", ""), " ", "\n"))## Joining, by = "fips"usmap::plot_usmap(regions="counties", include=c("TX", "OK"),
data=regionData[, c("fips", "region")], values="region") +
scale_fill_manual(values=c("red", "orange"), "", labels=c("Oklahoma", "Texas")) +
labs(title="Texas and Oklahoma Counties") +
geom_text(data=filter(regionData, abbr %in% c("TX", "OK")),
aes(x=x, y=y, label=cname), size=2.5,
color=ifelse(pull(filter(regionData, abbr %in% c("TX", "OK")), abbr)=="OK", "white", "black")
)
Example #5: Adding population centers
Separate data exists for key population centers, which can be loaded and then added to maps.
Example code includes:
# Transform the largest city data
str(maps::us.cities)## 'data.frame': 1005 obs. of 6 variables:
## $ name : chr "Abilene TX" "Akron OH" "Alameda CA" "Albany GA" ...
## $ country.etc: chr "TX" "OH" "CA" "GA" ...
## $ pop : int 113888 206634 70069 75510 93576 45535 494962 44933 127159 88857 ...
## $ lat : num 32.5 41.1 37.8 31.6 42.7 ...
## $ long : num -99.7 -81.5 -122.3 -84.2 -73.8 ...
## $ capital : int 0 0 0 0 2 0 0 0 0 0 ...trCity <- usmap::usmap_transform(select(maps::us.cities, long, lat, everything())) %>%
mutate(useName=str_replace_all(str_sub(name, 1, -4), " ", "\n"))
str(trCity)## 'data.frame': 1005 obs. of 9 variables:
## $ long : num -99.7 -81.5 -122.3 -84.2 -73.8 ...
## $ lat : num 32.5 41.1 37.8 31.6 42.7 ...
## $ name : chr "Abilene TX" "Akron OH" "Alameda CA" "Albany GA" ...
## $ country.etc: chr "TX" "OH" "CA" "GA" ...
## $ pop : int 113888 206634 70069 75510 93576 45535 494962 44933 127159 88857 ...
## $ capital : int 0 0 0 0 2 0 0 0 0 0 ...
## $ long.1 : num 24544 1533716 -1931842 1498524 2096820 ...
## $ lat.1 : num -1392670 -262400 -543195 -1350290 82431 ...
## $ useName : chr "Abilene" "Akron" "Alameda" "Albany" ...# Define a key region for plotting
rgnPlot <- c(usmap::.west_south_central, usmap::.east_south_central)
popFilter <- 100000
# Add cities as a layer to the state map
usmap::plot_usmap(regions="states", include=rgnPlot, fill="lightblue") +
labs(title=paste0("South Central Cities with Population >= ", popFilter/1000, "k")) +
geom_point(data=filter(trCity, pop >= popFilter, country.etc %in% rgnPlot),
aes(x=long.1, y=lat.1, size=pop), alpha=0.5
)
# Plot the full nation for cities of 250k +
rgnPlot <- c(usmap::.midwest_region, usmap::.northeast_region,
usmap::.south_region, usmap::.west_region
)
popFilter <- 250000
# Add cities as a layer to the state map
usmap::plot_usmap(regions="states", include=rgnPlot, fill="lightblue") +
labs(title=paste0("US Cities with Population >= ", popFilter/1000, "k")) +
geom_point(data=filter(trCity, pop >= popFilter, country.etc %in% rgnPlot),
aes(x=long.1, y=lat.1, size=pop), alpha=0.5
)
# Plot cities by name for the Four Corners region
rgnPlot <- c("UT", "CO", "NM", "AZ")
popFilter <- 125000
# Add cities as a layer to the state map (points)
usmap::plot_usmap(regions="counties", include=rgnPlot, fill="lightblue") +
labs(title=paste0("Four Corners Cities with Population >= ", popFilter/1000, "k")) +
geom_point(data=filter(trCity, pop >= popFilter, country.etc %in% rgnPlot),
aes(x=long.1, y=lat.1, size=pop), alpha=0.5
)
# Add cities as a layer to the state map (text)
usmap::plot_usmap(regions="counties", include=rgnPlot, fill="lightblue") +
labs(title=paste0("Four Corners Cities with Population >= ", popFilter/1000, "k")) +
geom_text(data=filter(trCity, pop >= popFilter, country.etc %in% rgnPlot),
aes(x=long.1, y=lat.1, size=pop, label=useName)
)
popFilter <- 50000
popFilter2 <- 250000
# Add cities as a layer to the state map (points and text)
usmap::plot_usmap(regions="counties", include=rgnPlot, fill="lightblue") +
labs(title=paste0("Four Corners Cities with Population >= ", popFilter/1000, "k")) +
geom_point(data=filter(trCity, pop >= popFilter, country.etc %in% rgnPlot),
aes(x=long.1, y=lat.1, size=pop), alpha=0.5
) +
geom_text(data=filter(trCity, pop >= popFilter2, country.etc %in% rgnPlot),
aes(x=long.1, y=lat.1, size=pop, label=useName), color="red", show.legend=FALSE
)
Example #6: Custom coloring geographies
Using scale_fill_manual(), custom colors can be created by geography.
Example code includes:
# Basic county population map with continuous colors
usmap::countypop %>%
filter(abbr %in% c("OH", "IN", "KY")) %>%
mutate(pop=pop_2015/1000, name=str_replace(str_replace(county, " County", ""), " ", "\n")) %>%
usmap::plot_usmap(regions="counties", include=c("OH", "IN", "KY"), data=., values="pop") +
scale_fill_continuous(low="lightblue", high="darkblue", "Pop. (000)") +
labs(title="Indiana, Ohio, and Kentucky - Population by County")
# Custom county population map with colors - red for Indiana, blue for Kentucky, grey for Ohio
popBucket <- c(0, 100, 500)
popLabels <- sapply(1:(length(popBucket)-1), FUN=function(x){paste0(popBucket[x], "-", popBucket[x+1])})
popLabels <- c(popLabels, paste0(popBucket[length(popBucket)], "+"))
guideLabels <- paste(rep(c("OH", "KY", "IN"), each=3), popLabels)
usmap::countypop %>%
filter(abbr %in% c("OH", "KY", "IN")) %>%
mutate(pop=pop_2015/1000, name=str_replace(str_replace(county, " County", ""), " ", "\n"),
pBucket=findInterval(pop, popBucket),
pColor=rgb(abbr=="IN", 0, abbr=="KY", pBucket/length(popBucket))
) %>%
usmap::plot_usmap(regions="counties", include=c("OH", "IN", "KY"), data=., values="pColor") +
scale_fill_identity(guide="legend", "Pop. (000)", labels=guideLabels) +
labs(title="Indiana, Ohio, and Kentucky - Population by County") +
theme(legend.position = "bottom") +
guides(fill=guide_legend(nrow=3))
# Custom county poverty rate map with colors - red for Indiana, blue for Kentucky, grey for Ohio
povBucket <- c(0, 15, 30)
povLabels <- sapply(1:(length(povBucket)-1), FUN=function(x){paste0(povBucket[x], "-", povBucket[x+1])})
povLabels <- c(povLabels, paste0(povBucket[length(povBucket)], "+"))
guideLabels <- paste(rep(c("OH", "KY", "IN"), each=3), povLabels)
usmap::countypov %>%
filter(abbr %in% c("OH", "KY", "IN")) %>%
mutate(name=str_replace(str_replace(county, " County", ""), " ", "\n"),
pBucket=findInterval(pct_pov_2014, povBucket),
pColor=rgb(abbr=="IN", 0, abbr=="KY", pBucket/length(povBucket))
) %>%
usmap::plot_usmap(regions="counties", include=c("OH", "IN", "KY"), data=., values="pColor") +
scale_fill_identity(guide="legend", "Poverty Rate (%)", labels=guideLabels) +
labs(title="Indiana, Ohio, and Kentucky - Poverty Rate by County") +
theme(legend.position = "bottom") +
guides(fill=guide_legend(nrow=3))
Example #7: Custom labeling of key geographies
The above techniques can be combined for custom labeling of key geographies.
Example code includes:
# Basic state population data
stateData <- usmap::statepop %>%
mutate(pop=round(pop_2015/1000000, 1),
name=ifelse(full=="District of Columbia", "DC", str_replace(full, " ", "\n")),
lab=paste0(abbr, "\n(", pop, ")\n")
)
# Load state centroid data
stCenter <- utils::read.csv(system.file("extdata",
paste0("us_", "states", "_centroids.csv"), package = "usmap"
),
colClasses = c(rep("numeric", 2), rep("character", 3)), stringsAsFactors = FALSE
)
# Grab centroids for top 5 states
top5State <- stateData %>%
top_n(5, pop) %>%
left_join(select(stCenter, x, y, fips))## Joining, by = "fips"# Plot state population with continuous colors and custom labels
stateData %>%
usmap::plot_usmap(regions="states", data=., values="pop") +
scale_fill_continuous(low="lightblue", high="darkblue", "Pop. (millions)") +
labs(title="Population by State", subtitle="Top 5 in millions") +
geom_text(data=top5State, aes(x=x, y=y, label=lab), color="white", size=4, fontface="bold")
# Load county centroid data
ctCenter <- utils::read.csv(system.file("extdata",
paste0("us_", "counties", "_centroids.csv"), package = "usmap"
),
colClasses = c(rep("numeric", 2), rep("character", 4)), stringsAsFactors = FALSE
)
# Custom county population map with colors - red for Wisconsin, blue for Michigan
popBucket <- c(0, 100, 500)
popLabels <- sapply(1:(length(popBucket)-1), FUN=function(x){paste0(popBucket[x], "-", popBucket[x+1])})
popLabels <- c(popLabels, paste0(popBucket[length(popBucket)], "+"))
guideLabels <- paste(rep(c("MI", "WI"), each=3), popLabels)
# Grab county data for counties exceeding the top popBucket
ctyData <- usmap::countypop %>%
filter(abbr %in% c("MI", "WI")) %>%
mutate(pop=round(pop_2015/1000, 0),
name=str_replace(str_replace(county, " County", ""), " ", "\n"),
lab=paste0(name, "\n(", pop, ")\n")
)
topCounty <- ctyData %>%
filter(pop >= max(popBucket)) %>%
left_join(select(ctCenter, x, y, fips))## Joining, by = "fips"# Create county population map
usmap::countypop %>%
filter(abbr %in% c("MI", "WI")) %>%
mutate(pop=pop_2015/1000, name=str_replace(str_replace(county, " County", ""), " ", "\n"),
pBucket=findInterval(pop, popBucket),
pColor=rgb(abbr=="WI", 0, abbr=="MI", pBucket/length(popBucket))
) %>%
usmap::plot_usmap(regions="counties", include=c("WI", "MI"), data=., values="pColor") +
scale_fill_identity(guide="legend", "Pop. (000)", labels=guideLabels) +
geom_text(data=topCounty, aes(x=x, y=y, label=lab), size=3, fontface="bold", color="white") +
labs(title="Michigan and Wisconsin - Population by County", subtitle="Labelled Pop. (000) for 500k+") +
theme(legend.position = "bottom") +
guides(fill=guide_legend(nrow=3)) +
theme(panel.background=element_rect(color="black", fill="lightgrey"))
Example #8: Plotting Weather Data (Temperatures and Dew Points)
The ggridges package has weather data for Lincoln, NE in the data file ‘lincoln_weather’. The data are captured once per day for 366 days of 2016. Simple plots can be made of the average temperatures and dew points.
Example code includes:
data(lincoln_weather, package="ggridges")
str(lincoln_weather, give.attr=FALSE)## Classes 'tbl_df', 'tbl' and 'data.frame': 366 obs. of 24 variables:
## $ CST : chr "2016-1-1" "2016-1-2" "2016-1-3" "2016-1-4" ...
## $ Max Temperature [F] : int 37 41 37 30 38 34 33 28 22 31 ...
## $ Mean Temperature [F] : int 24 23 23 17 29 33 30 25 9 11 ...
## $ Min Temperature [F] : int 11 5 8 4 19 32 27 22 -4 -9 ...
## $ Max Dew Point [F] : int 19 22 23 24 29 33 32 25 17 20 ...
## $ Mean Dew Point [F] : int 13 14 15 13 25 32 30 22 4 5 ...
## $ Min Dewpoint [F] : int 8 4 8 2 19 29 25 18 -8 -13 ...
## $ Max Humidity : int 88 100 92 92 96 100 100 92 87 87 ...
## $ Mean Humidity : int 68 72 73 82 83 91 96 85 77 75 ...
## $ Min Humidity : int 47 44 54 72 70 82 92 78 67 63 ...
## $ Max Sea Level Pressure [In] : num 30.5 30.4 30.5 30.5 30.2 ...
## $ Mean Sea Level Pressure [In]: num 30.4 30.3 30.4 30.4 30.1 ...
## $ Min Sea Level Pressure [In] : num 30.3 30.2 30.3 30.2 30 ...
## $ Max Visibility [Miles] : int 10 10 10 10 10 10 9 10 10 10 ...
## $ Mean Visibility [Miles] : int 10 10 10 9 8 4 3 6 9 10 ...
## $ Min Visibility [Miles] : int 10 10 10 6 5 0 0 2 5 10 ...
## $ Max Wind Speed [MPH] : int 20 15 13 17 22 16 16 25 25 10 ...
## $ Mean Wind Speed[MPH] : int 9 6 5 7 13 7 7 16 14 5 ...
## $ Max Gust Speed [MPH] : int 23 18 14 23 28 21 21 32 28 12 ...
## $ Precipitation [In] : chr "0" "0" "0" "0" ...
## $ CloudCover : int 0 0 0 1 4 8 8 8 5 0 ...
## $ Events : chr NA NA NA NA ...
## $ WindDir [Degrees] : int 280 312 330 155 178 167 7 338 340 268 ...
## $ Month : Factor w/ 12 levels "December","November",..: 12 12 12 12 12 12 12 12 12 12 ...# Extract temperature and dew point data
tdData <- lincoln_weather %>%
select(CST, maxT=`Max Temperature [F]`, minT=`Min Temperature [F]`, meanT=`Mean Temperature [F]`,
maxD=`Max Dew Point [F]`, minD=`Min Dewpoint [F]`, meanD=`Mean Dew Point [F]`
) %>%
mutate(date=as.Date(CST))
str(tdData)## Classes 'tbl_df', 'tbl' and 'data.frame': 366 obs. of 8 variables:
## $ CST : chr "2016-1-1" "2016-1-2" "2016-1-3" "2016-1-4" ...
## $ maxT : int 37 41 37 30 38 34 33 28 22 31 ...
## $ minT : int 11 5 8 4 19 32 27 22 -4 -9 ...
## $ meanT: int 24 23 23 17 29 33 30 25 9 11 ...
## $ maxD : int 19 22 23 24 29 33 32 25 17 20 ...
## $ minD : int 8 4 8 2 19 29 25 18 -8 -13 ...
## $ meanD: int 13 14 15 13 25 32 30 22 4 5 ...
## $ date : Date, format: "2016-01-01" "2016-01-02" ...# Plot temperatures by day
tdData %>%
select(date, maxT, meanT, minT) %>%
pivot_longer(-date) %>%
ggplot(aes(x=date, y=value, group=name)) +
geom_line(aes(color=name))
# Plot dew points by day
tdData %>%
select(date, maxD, meanD, minD) %>%
pivot_longer(-date) %>%
ggplot(aes(x=date, y=value, group=name)) +
geom_line(aes(color=name))
library(xts)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following objects are masked from 'package:dplyr':
##
## first, last# Create an XTS for temperature data
tdXTS <- xts(select(tdData, minT, meanT, maxT), order.by=tdData$date)
# Create and plot weekly and monthly averages
tdXTS %>%
apply.weekly(FUN=mean, na.rm=TRUE) %>%
plot(main="Weekly Temperature Average (Lincoln, NE 2016)")
tdXTS %>%
apply.monthly(FUN=mean, na.rm=TRUE) %>%
plot(main="Monthly Temperature Average (Lincoln, NE 2016)")
# Create an XTS for dew-point data
tdXTS <- xts(select(tdData, minD, meanD, maxD), order.by=tdData$date)
# Create and plot weekly and monthly averages
tdXTS %>%
apply.weekly(FUN=mean, na.rm=TRUE) %>%
plot(main="Weekly Dew Point Average (Lincoln, NE 2016)")
tdXTS %>%
apply.monthly(FUN=mean, na.rm=TRUE) %>%
plot(main="Monthly Dew Point Average (Lincoln, NE 2016)")
Example #9: Combining xts and ggplot2
The xts package is good for working with time series data, while ggplot2 is strong for customizing plots. The packages can be combined in using the weather data.
Example code includes:
# Create an XTS for temperature and dewpoint data
tdXTS <- xts(select(tdData, minT, meanT, maxT, minD, meanD, maxD), order.by=tdData$date)
# Use xts for monthly average and ggplot2 for plotting
basePlot <- tdXTS %>%
apply.monthly(FUN=mean, na.rm=TRUE) %>%
data.frame(date=index(.), row.names=NULL) %>%
ggplot(aes(x=date-lubridate::days(15))) +
geom_ribbon(aes(ymin=minT, ymax=maxT), color="lightblue", fill="lightblue", alpha=0.5) +
geom_line(aes(y=meanT), color="blue", lwd=1) +
labs(x="Month", y="Avg. Temperature (F)", title="Lincoln, NE Weather (2016)",
subtitle="Monthly Avg. Temperature (F)"
)
basePlot
# Add labelling for the three elements
hiMonth <- index(tdXTS %>% apply.monthly(FUN=mean))[3]
loMonth <- index(tdXTS %>% apply.monthly(FUN=mean))[9]
muMonth <- index(tdXTS %>% apply.monthly(FUN=mean))[6]
hiPoint <- c(60, 75)
loPoint <- c(45, 25)
muPoint <- c(72.5, 45)
labFrame <- data.frame(x=c(hiMonth, loMonth, muMonth),
yend=c(hiPoint[1], loPoint[1], muPoint[1]),
y=c(hiPoint[2], loPoint[2], muPoint[2]),
text=c("Avg. Monthly High", "Avg. Monthly Low", "Avg. Monthly Mean")) %>%
mutate(xend=x)
basePlot +
geom_segment(data=labFrame, aes(x=x, y=y, xend=xend, yend=yend), arrow=arrow()) +
geom_text(data=labFrame, aes(x=x, y=y+c(5, -5, -5), label=text), fontface="bold", size=4)
# Can also create and plot a custom rolling average
baseData <- tdXTS %>%
data.frame(date=index(.), row.names=NULL)
base7Day <- rollapply(tdXTS, 7, FUN=mean, na.rm=TRUE) %>%
data.frame(date=index(.), row.names=NULL)
base30Day <- rollapply(tdXTS, 30, FUN=mean, na.rm=TRUE) %>%
data.frame(date=index(.), row.names=NULL)
plotFrame <- bind_rows(baseData, base7Day, base30Day, .id="rolling") %>%
mutate(rollLabel=case_when(rolling==1 ~ "Daily",
rolling==2 ~ "7 Day Rolling",
rolling==3 ~ "30 Day Rolling",
TRUE ~ "ERROR"
)
)
plotFrame %>%
ggplot(aes(x=date)) +
geom_line(aes(y=meanT, color=rollLabel, group=rollLabel), lwd=1) +
labs(x="Month", y="Avg. Temperature (F)", title="Lincoln, NE Weather (2016)",
subtitle="Daily Avg. Temperature (F)"
)## Warning: Removed 35 rows containing missing values (geom_path).
Example #10: Plotting Weather Data (Humidity)
Humidity data are also available in the lincoln_weather dataset. There is a relationship between temperature, dewpoint, and humidity.
Example code includes:
htdData <- lincoln_weather %>%
select(CST, meanT=`Mean Temperature [F]`, meanD=`Mean Dew Point [F]`, meanH=`Mean Humidity`) %>%
mutate(date=as.Date(CST), month=lubridate::month(date))
str(htdData)## Classes 'tbl_df', 'tbl' and 'data.frame': 366 obs. of 6 variables:
## $ CST : chr "2016-1-1" "2016-1-2" "2016-1-3" "2016-1-4" ...
## $ meanT: int 24 23 23 17 29 33 30 25 9 11 ...
## $ meanD: int 13 14 15 13 25 32 30 22 4 5 ...
## $ meanH: int 68 72 73 82 83 91 96 85 77 75 ...
## $ date : Date, format: "2016-01-01" "2016-01-02" ...
## $ month: num 1 1 1 1 1 1 1 1 1 1 ...# Histogram for average humidity
htdData %>%
ggplot(aes(x=meanH)) +
geom_histogram() +
labs(title="Mean Humidity Histogram", subtitle="Lincoln, NE (2016)", x="Mean Humidity (%)", y="Count")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
htdData %>%
filter((meanH < 25) | is.na(meanH))## # A tibble: 2 x 6
## CST meanT meanD meanH date month
## <chr> <int> <int> <int> <date> <dbl>
## 1 2016-2-27 50 21 0 2016-02-27 2
## 2 2016-2-28 44 NA NA 2016-02-28 2htdData <- htdData %>%
filter(!((meanH < 25) | is.na(meanH)))
# Updated Histogram for average humidity
htdData %>%
ggplot(aes(x=meanH)) +
geom_histogram() +
labs(title="Mean Humidity Histogram", subtitle="Lincoln, NE (2016)", x="Mean Humidity (%)", y="Count")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Histogram for dewpoint depression (T - D)
htdData %>%
ggplot(aes(x=meanT-meanD)) +
geom_histogram() +
labs(title="Mean Dewpoint Depression Histogram", subtitle="Lincoln, NE (2016)",
x="Mean Dewpoint Depression (F)", y="Count")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
htdData %>%
filter(meanD >= meanT)## # A tibble: 2 x 6
## CST meanT meanD meanH date month
## <chr> <int> <int> <int> <date> <dbl>
## 1 2016-1-7 30 30 96 2016-01-07 1
## 2 2016-11-27 37 39 85 2016-11-27 11htdData <- htdData %>%
filter(meanT >= meanD)
# Updated Histogram for dewpoint depression (T - D)
htdData %>%
ggplot(aes(x=meanT-meanD, y=..density..)) +
geom_histogram(binwidth=1) +
geom_density(color="red") +
labs(title="Mean Dewpoint Depression Histogram", subtitle="Lincoln, NE (2016)",
x="Mean Dewpoint Depression (F)", y="Proportion")
# Average humidity by month
htdData %>%
group_by(month) %>%
summarize(meanH=mean(meanH, na.rm=TRUE)) %>%
ggplot(aes(x=as.factor(month), y=meanH)) +
geom_col() +
labs(title="Average Humidity by Month", subtitle="Lincoln, NE (2016)", x="Month", y="Mean Humidity (%)")
# Relationship between temperature and dewpoint
htdData %>%
ggplot(aes(x=meanD, y=meanT)) +
geom_point() +
geom_abline(slope=1, intercept=0) +
labs(title="Daily Averages", subtitle="Lincoln, NE (2016)",
x="Mean Dewpoint (F)", y="Mean Temperature (F)"
)
# Relationship between dewpoint depression and humidity
htdData %>%
mutate(dpD=meanT-meanD) %>%
ggplot(aes(x=dpD, y=meanH)) +
geom_point() +
labs(title="Daily Averages", subtitle="Lincoln, NE (2016)",
x="Mean Dewpoint Depression (F)", y="Mean Humidity (%)"
)
# Relationship between temperature and dewpoint and humidity
humInts <- c(0, 50, 60, 70, 80)
humLabel <- sapply(1:(length(humInts)-1), FUN=function(x) { paste0(humInts[x], "-", humInts[x+1]) })
humLabel <- c(humLabel, paste0(humInts[length(humInts)], "+"))
htdData %>%
mutate(humBin=factor(findInterval(meanH, humInts), levels=1:length(humInts), labels=humLabel)) %>%
ggplot(aes(x=meanD, y=meanT, color=humBin)) +
geom_point() +
geom_smooth(se=FALSE) +
geom_abline(slope=1, intercept=0) +
labs(title="Daily Averages", subtitle="Lincoln, NE (2016)",
x="Mean Dewpoint (F)", y="Mean Temperature (F)"
)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
# Expressed using dewpoint depression vs. dewpoint
htdData %>%
mutate(humBin=factor(findInterval(meanH, humInts), levels=1:length(humInts), labels=humLabel)) %>%
ggplot(aes(x=meanD, y=meanT-meanD, color=humBin)) +
geom_point() +
geom_smooth() +
labs(title="Daily Averages", subtitle="Lincoln, NE (2016)",
x="Mean Dewpoint (F)", y="Dewpoint Depression (F)"
)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
# Expressed using dewpoint depression vs. temperature
htdData %>%
mutate(humBin=factor(findInterval(meanH, humInts), levels=1:length(humInts), labels=humLabel)) %>%
ggplot(aes(x=meanT, y=meanT-meanD, color=humBin)) +
geom_point() +
geom_smooth(se=FALSE, method="lm") +
labs(title="Daily Averages", subtitle="Lincoln, NE (2016)",
x="Mean Temperature (F)", y="Dewpoint Depression (F)"
)
# Linear regression for temperature, dewpoint, and humidity
htdReg <- htdData %>%
mutate(dpD=meanT-meanD) %>%
lm(meanH ~ meanT + dpD, data=.)
summary(htdReg)##
## Call:
## lm(formula = meanH ~ meanT + dpD, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.3569 -2.5472 -0.1894 2.7285 14.4934
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 82.89890 0.83037 99.833 < 2e-16 ***
## meanT 0.09771 0.01403 6.965 1.57e-11 ***
## dpD -1.79530 0.04811 -37.317 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.869 on 360 degrees of freedom
## Multiple R-squared: 0.796, Adjusted R-squared: 0.7949
## F-statistic: 702.4 on 2 and 360 DF, p-value: < 2.2e-16htdData %>%
mutate(dpD=meanT-meanD) %>%
mutate(predH=predict(htdReg, newdata=.)) %>%
ggplot(aes(x=predH, y=meanH)) +
geom_point() +
geom_abline(slope=1, intercept=0) +
labs(title="Daily Averages", subtitle="Lincoln, NE (2016)",
x="Predicted Humidity (%)", y="Actual Humidity (%)"
)
Example #11: Plotting Weather Data (Wind)
Wind data (speed, gust, direction) are also available in the lincoln_weather dataset..
Example code includes:
# Extract wind data
wdData <- lincoln_weather %>%
select(CST, maxW=`Max Wind Speed [MPH]`, maxG=`Max Gust Speed [MPH]`, meanW=`Mean Wind Speed[MPH]`,
dirW=`WindDir [Degrees]`
) %>%
mutate(date=as.Date(CST))
str(wdData)## Classes 'tbl_df', 'tbl' and 'data.frame': 366 obs. of 6 variables:
## $ CST : chr "2016-1-1" "2016-1-2" "2016-1-3" "2016-1-4" ...
## $ maxW : int 20 15 13 17 22 16 16 25 25 10 ...
## $ maxG : int 23 18 14 23 28 21 21 32 28 12 ...
## $ meanW: int 9 6 5 7 13 7 7 16 14 5 ...
## $ dirW : int 280 312 330 155 178 167 7 338 340 268 ...
## $ date : Date, format: "2016-01-01" "2016-01-02" ...# Manage missing data
wdData[!complete.cases(wdData), ]## # A tibble: 6 x 6
## CST maxW maxG meanW dirW date
## <chr> <int> <int> <int> <int> <date>
## 1 2016-2-6 18 NA 5 241 2016-02-06
## 2 2016-2-28 36 45 18 NA 2016-02-28
## 3 2016-5-31 17 NA 11 319 2016-05-31
## 4 2016-6-18 NA NA NA -1 2016-06-18
## 5 2016-6-19 NA NA NA -1 2016-06-19
## 6 2016-12-19 17 NA 9 209 2016-12-19wdData <- wdData %>%
filter(dirW != -1, !is.na(dirW)) %>%
mutate(maxG=ifelse(is.na(maxG), maxW, maxG))
summary(wdData)## CST maxW maxG meanW
## Length:363 Min. : 8.00 Min. : 10.00 Min. : 0.00
## Class :character 1st Qu.: 16.00 1st Qu.: 21.00 1st Qu.: 6.00
## Mode :character Median : 21.00 Median : 26.00 Median : 8.00
## Mean : 21.71 Mean : 27.93 Mean : 9.11
## 3rd Qu.: 25.00 3rd Qu.: 33.00 3rd Qu.:11.00
## Max. :131.00 Max. :143.00 Max. :27.00
## dirW date
## Min. : 1.0 Min. :2016-01-01
## 1st Qu.:143.5 1st Qu.:2016-04-01
## Median :193.0 Median :2016-07-03
## Mean :209.8 Mean :2016-07-01
## 3rd Qu.:310.0 3rd Qu.:2016-10-01
## Max. :358.0 Max. :2016-12-31# Manage very high wind data
wdData[wdData$maxG >= 60, ]## # A tibble: 3 x 6
## CST maxW maxG meanW dirW date
## <chr> <int> <int> <int> <int> <date>
## 1 2016-3-23 45 61 25 20 2016-03-23
## 2 2016-6-17 131 143 20 170 2016-06-17
## 3 2016-12-25 51 64 19 139 2016-12-25wdData <- wdData %>%
filter(maxG <= 80)
summary(wdData)## CST maxW maxG meanW
## Length:362 Min. : 8.0 Min. :10.00 Min. : 0.00
## Class :character 1st Qu.:16.0 1st Qu.:21.00 1st Qu.: 6.00
## Mode :character Median :21.0 Median :26.00 Median : 8.00
## Mean :21.4 Mean :27.61 Mean : 9.08
## 3rd Qu.:25.0 3rd Qu.:33.00 3rd Qu.:11.00
## Max. :51.0 Max. :64.00 Max. :27.00
## dirW date
## Min. : 1.0 Min. :2016-01-01
## 1st Qu.:143.2 1st Qu.:2016-04-01
## Median :193.0 Median :2016-07-03
## Mean :209.9 Mean :2016-07-01
## 3rd Qu.:310.0 3rd Qu.:2016-10-01
## Max. :358.0 Max. :2016-12-31# Density of wind speeds
wdData %>%
select(date, meanW, maxW, maxG) %>%
pivot_longer(-date) %>%
ggplot(aes(x=value, fill=name)) +
geom_density(alpha=0.5) +
scale_fill_discrete(name="Wind Speed [MPH]", labels=c("Max Gust", "Max", "Mean")) +
labs(title="Lincoln, NE (2016) Wind Speeds", y="Density", x="Wind Speed [MPH]")
# Density of wind direction
wdData %>%
select(date, dirW) %>%
ggplot(aes(x=dirW)) +
geom_density(alpha=0.5, fill="blue") +
labs(title="Winds are mainly from the S and NW", subtitle="Lincoln, NE (2016)",
y="Density", x="Wind Direction"
)
# Wind speed and direction
wdData %>%
ggplot(aes(x=meanW, y=dirW)) +
geom_point(alpha=0.25) +
coord_polar(theta="y") +
labs(title="Lincoln, NE (2016)", subtitle="Direction vs. Mean Wind Speed", x="Mean Wind Speed [MPH]") +
scale_y_continuous(limits=c(0, 360), breaks=c(0, 90, 180, 270, 360)) +
scale_x_continuous(limits=c(0, 30), breaks=c(0, 5, 10, 15, 20, 25, 30)) +
geom_point(aes(x=0, y=0), color="red", size=2)
# Wind speed and direction as factors
windDirs <- c("N", "NE", "E", "SE", "S", "SW", "W", "NW")
windSpeeds <- c(0, 5, 10, 15)
windLabels <- sapply(1:(length(windSpeeds)-1), FUN=function(x){ paste0(windSpeeds[x], "-", windSpeeds[x+1]) })
windLabels <- c(windLabels, paste0(windSpeeds[length(windSpeeds)], "+"))
wdData <- wdData %>%
mutate(wd=factor(floor(((dirW+22.5)/45) %% 8), levels=0:7, labels=windDirs),
ws=factor(findInterval(meanW, windSpeeds), levels=length(windSpeeds):1, labels=rev(windLabels))
)
# Summary of interaction between wind speed and wind direction
wdData %>%
group_by(wd) %>%
summarize(n=n(), avgMean=mean(meanW), avgMax=mean(maxW), avgGust=mean(maxG))## # A tibble: 8 x 5
## wd n avgMean avgMax avgGust
## <fct> <int> <dbl> <dbl> <dbl>
## 1 N 51 10.1 21.4 27.7
## 2 NE 18 8.17 19.4 24.8
## 3 E 16 7.44 18.9 24.6
## 4 SE 69 8.01 19.6 25.5
## 5 S 73 8.89 20.6 26.8
## 6 SW 26 6.96 20.3 26.2
## 7 W 29 8.10 20.4 26.5
## 8 NW 80 11.1 25.4 32.2table(wdData$wd, wdData$ws)##
## 15+ 10-15 5-10 0-5
## N 8 14 25 4
## NE 1 7 6 4
## E 0 3 12 1
## SE 3 15 42 9
## S 5 23 43 2
## SW 0 6 15 5
## W 3 4 18 4
## NW 17 31 28 4# Graph of wind speed and wind direction
wdData %>%
ggplot(aes(x=wd, fill=ws)) +
geom_bar() +
scale_fill_discrete(name="Wind Speed [MPH]") +
labs(title="Lincoln, NE (2016) Wind Speeds and Directions", y="# Days", x="Wind Direction")
# Graph of wind speed and wind direction (polar coordinates)
wdData %>%
ggplot(aes(x=wd, fill=ws)) +
geom_bar() +
scale_fill_discrete(name="Wind Speed [MPH]") +
labs(title="Lincoln, NE (2016) Wind Speeds and Directions", y="# Days", x="Wind Direction") +
coord_polar(start=-0.4)
Example #12: Archived granular weather Data (METAR)
Iowa State has a great database of archived weather data, including the historical METAR data (meteorological aerodrome report) for a number of reporting stations.
METAR include information on visibility, wind, temperature, dew point, precipitation, clouds, barometric pressure, and other features that may impact safe aviation.
The data for station KLNK (Lincoln, NE airport) was saved as a CSV from Iowa State
Some processing is required before using the METAR data:
Example code includes:
# Load METAR data
klnk <- readr::read_csv("./RInputFiles/metar_klnk_2016.txt", na=c("", "NA", "M"))## Parsed with column specification:
## cols(
## .default = col_double(),
## station = col_character(),
## valid = col_datetime(format = ""),
## p01i = col_character(),
## skyc1 = col_character(),
## skyc2 = col_character(),
## skyc3 = col_character(),
## skyc4 = col_logical(),
## skyl4 = col_logical(),
## wxcodes = col_character(),
## ice_accretion_1hr = col_character(),
## ice_accretion_3hr = col_character(),
## ice_accretion_6hr = col_character(),
## peak_wind_time = col_datetime(format = ""),
## metar = col_character()
## )## See spec(...) for full column specifications.str(klnk, give.attr=FALSE)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 10594 obs. of 29 variables:
## $ station : chr "LNK" "LNK" "LNK" "LNK" ...
## $ valid : POSIXct, format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## $ tmpf : num 27 26.1 27 27 27 ...
## $ dwpf : num 19.9 19.9 19.9 21 19.9 ...
## $ relh : num 74.5 77.3 74.5 78 74.5 ...
## $ drct : num 300 0 0 280 310 10 0 10 20 0 ...
## $ sknt : num 5 0 0 3 5 9 0 3 3 0 ...
## $ p01i : chr "0.00" "0.00" "0.00" "0.00" ...
## $ alti : num 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num 1028 1028 1028 1028 1029 ...
## $ vsby : num 10 10 10 10 10 10 10 10 10 10 ...
## $ gust : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr "OVC" "OVC" "OVC" "OVC" ...
## $ skyc2 : chr NA NA NA NA ...
## $ skyc3 : chr NA NA NA NA ...
## $ skyc4 : logi NA NA NA NA NA NA ...
## $ skyl1 : num 2800 2700 2600 2700 2100 2700 2700 2700 2600 2600 ...
## $ skyl2 : num NA NA NA NA 2700 NA NA NA NA NA ...
## $ skyl3 : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyl4 : logi NA NA NA NA NA NA ...
## $ wxcodes : chr NA NA NA NA ...
## $ ice_accretion_1hr: chr NA NA NA NA ...
## $ ice_accretion_3hr: chr NA NA NA NA ...
## $ ice_accretion_6hr: chr NA NA NA NA ...
## $ peak_wind_gust : num NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_drct : num NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_time : POSIXct, format: NA NA ...
## $ feel : num 20.4 26.1 27 22.9 20.4 ...
## $ metar : chr "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...# Filter to only data that ends with times ending in 54Z
metarKLNK <- klnk %>%
filter(str_detect(metar, "54Z"))
dim(metarKLNK)## [1] 8813 29# There should be 24*368=8832 records, so there are a handful (19) of missing METAR observations
minDate <- min(metarKLNK$valid)
expDate <- minDate + lubridate::hours(0:(24*368 - 1))
# Observations expected but not recorded
as.POSIXct(setdiff(expDate, metarKLNK$valid), origin="1970-01-01", tz="UTC")## [1] "2016-01-19 11:54:00 UTC" "2016-05-06 11:54:00 UTC"
## [3] "2016-05-06 12:54:00 UTC" "2016-06-17 23:54:00 UTC"
## [5] "2016-06-18 00:54:00 UTC" "2016-06-18 07:54:00 UTC"
## [7] "2016-07-02 15:54:00 UTC" "2016-07-13 14:54:00 UTC"
## [9] "2016-07-13 15:54:00 UTC" "2016-07-13 16:54:00 UTC"
## [11] "2016-07-13 17:54:00 UTC" "2016-07-30 13:54:00 UTC"
## [13] "2016-08-02 07:54:00 UTC" "2016-08-05 07:54:00 UTC"
## [15] "2016-08-29 21:54:00 UTC" "2016-09-15 16:54:00 UTC"
## [17] "2016-09-16 05:54:00 UTC" "2016-11-21 00:54:00 UTC"
## [19] "2016-12-03 08:54:00 UTC"# Observations recorded but not expected
setdiff(metarKLNK$valid, expDate)## numeric(0)# Confirmation of uniqueness
length(unique(metarKLNK$valid)) == length(metarKLNK$valid)## [1] TRUE# Extract wind speeds and direction
# The general wind format is dddssGssKT where ddd is the direction (VRB meaning variable), the main ss is the speed, and the Gss is the gust speed (optional and not always displayed)
mtxWind <- metarKLNK %>%
pull(metar) %>%
str_match(pattern="(\\d{3}|VRB)(\\d{2})(G\\d{2})?KT")
head(mtxWind)## [,1] [,2] [,3] [,4]
## [1,] "30005KT" "300" "05" NA
## [2,] "00000KT" "000" "00" NA
## [3,] "00000KT" "000" "00" NA
## [4,] "28003KT" "280" "03" NA
## [5,] "31005KT" "310" "05" NA
## [6,] "01009KT" "010" "09" NAtable(mtxWind[, 2], useNA="ifany")##
## 000 010 020 030 040 050 060 070 080 090 100 110 120 130 140 150
## 875 269 199 146 135 121 108 95 88 65 102 158 169 245 241 339
## 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310
## 463 565 517 413 284 179 142 96 73 80 105 89 121 141 147 225
## 320 330 340 350 360 VRB <NA>
## 234 303 352 413 383 114 19table(mtxWind[, 3], useNA="ifany")##
## 00 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17
## 875 615 704 664 696 651 636 599 536 451 439 371 315 276 235 173
## 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 34
## 156 108 69 62 45 33 23 13 14 9 10 6 6 2 1 1
## <NA>
## 19table(mtxWind[, 4], useNA="ifany")##
## G14 G15 G16 G17 G18 G19 G20 G21 G22 G23 G24 G25 G26 G27 G28 G29
## 6 11 11 16 30 59 69 87 83 107 101 83 62 61 61 37
## G30 G31 G32 G33 G34 G35 G36 G37 G38 G39 G40 G41 G42 G43 G45 <NA>
## 28 24 18 13 12 14 6 6 10 11 2 3 1 2 2 7777# Verify that winds not captured are in fact missing from the METAR
metarKLNK[which(is.na(mtxWind[, 2])), "metar"]## # A tibble: 19 x 1
## metar
## <chr>
## 1 KLNK 291354Z 10SM CLR M01/M03 A2976 RMK AO2 SLP088 T10061028
## 2 KLNK 012154Z 10SM CLR 01/M08 A3018 RMK AO2 SLP234 T00061083
## 3 KLNK 201754Z 10SM CLR 06/M07 A3041 RMK AO2 SLP308 T00611067 10061 21039 58013
## 4 KLNK 221754Z 10SM CLR 16/03 A2955 RMK AO2 SLP000 T01610033 10161 20106 58006~
## 5 KLNK 221854Z 10SM CLR 17/03 A2954 RMK AO2 SLP000 T01670033 $
## 6 KLNK 050254Z 10SM CLR 14/03 A3004 RMK AO2 SLP169 T01390033 53007
## 7 KLNK 181754Z 10SM OVC075 21/04 A3023 RMK AO2 SLP234 T02110039 10217 20072 58~
## 8 KLNK 011454Z 21/10 A3008 RMK AO2 SLP178 T02110100 51011 $
## 9 KLNK 152254Z 10SM CLR 38/11 A2978 RMK AO2 SLP070 T03780106
## 10 KLNK 181954Z 10SM SCT045 31/20 A3017 RMK AO2 SLP205 T03060200
## 11 KLNK 261654Z 10SM CLR 29/18 A3014 RMK AO2 SLP192 T02890183 $
## 12 KLNK 261754Z 10SM CLR 31/18 A3012 RMK AO2 SLP188 T03060178 10306 20222
## 13 KLNK 271754Z 10SM CLR 33/18 A3015 RMK AO2 SLP198 T03330183 10333 20222 55001
## 14 KLNK 251754Z 10SM CLR 29/17 A3013 RMK AO2 SLP192 T02890167 10289 20194 58010
## 15 KLNK 261854Z 10SM CLR 30/18 A3006 RMK AO2 SLP166 T03000183
## 16 KLNK 211854Z 10SM CLR 27/10 A3007 RMK AO2 SLP174 T02670100
## 17 KLNK 201454Z 10SM CLR 24/21 A3008 RMK AO2 SLP178 T02390206 56004 $
## 18 KLNK 051854Z 10SM CLR 22/07 A3027 RMK AO2 SLP244 T02170067
## 19 KLNK 170754Z AUTO 10SM CLR 08/05 A2950 RMK AO2 SLP984 T00830050metarKLNK <- metarKLNK %>%
mutate(dirW=mtxWind[, 2],
spdW=as.numeric(mtxWind[, 3]),
gustW=as.numeric(str_replace(mtxWind[, 4], "G", ""))
)
# Plot for the wind direction
metarKLNK %>%
ggplot(aes(x=dirW)) +
geom_bar() +
labs(title="Lincoln, NE Wind Direction", subtitle="KLNK METAR (2016)",
y="# Hourly Observations", x="Wind Direction"
)
# Plot for the minimum, average, and maximum wind speed by wind direction
# Wind direction 000 is reserved for 0 KT wind, while VRB is reserved for 3-6 KT variable winds
metarKLNK %>%
filter(!is.na(dirW)) %>%
group_by(dirW) %>%
summarize(minWind=min(spdW), meanWind=mean(spdW), maxWind=max(spdW)) %>%
ggplot(aes(x=dirW)) +
geom_point(aes(y=meanWind), color="red", size=2) +
geom_errorbar(aes(ymin=minWind, ymax=maxWind)) +
labs(title="Lincoln, NE Wind Direction", subtitle="KLNK METAR (2016)",
y="Wind Speed [KT]", x="Wind Direction"
)
# Plot for the wind speed
# Roughly 10% of the time, there is no wind in Lincoln
metarKLNK %>%
ggplot(aes(x=spdW)) +
geom_bar(aes(y=..count../sum(..count..))) +
labs(title="Roughly 10% of wind speeds in Lincoln, NE measure 0 Knots", subtitle="KLNK METAR (2016)",
y="% Hourly Observations", x="Wind Speed {KT]"
)## Warning: Removed 19 rows containing non-finite values (stat_count).
metarKLNK %>%
filter(!is.na(dirW), dirW != "VRB", dirW != "000") %>%
mutate(dirW=as.numeric(dirW)) %>%
group_by(dirW, spdW) %>%
summarize(n=n()) %>%
ggplot(aes(x=spdW, y=dirW)) +
geom_point(alpha=0.1, aes(size=n)) +
coord_polar(theta="y") +
labs(title="Lincoln, NE (2016)", subtitle="Direction vs. Wind Speed", x="Wind Speed [KT]") +
scale_y_continuous(limits=c(0, 360), breaks=c(0, 90, 180, 270, 360)) +
scale_x_continuous(limits=c(0, 30), breaks=c(0, 5, 10, 15, 20, 25, 30)) +
geom_point(aes(x=0, y=0), color="red", size=2)## Warning: Removed 4 rows containing missing values (geom_point).
Example #13: Extracting Key Elements from METAR
A properly formatted METAR includes the following information in order, though with variable amounts of other information in between.
dddd54Z ddddd[Gdd]KT dSM [M]dd/[M]dd Adddd RMK SLPddd Tdddddddd
- dddd54Z is the two-digit date and four-digit Zulu time (KLNK METAR are taken at 54 minutes past the hour)
- ddddd[Gdd]KT is the three-digit wind direction (can be VRB), two digit wind speed in knots, and sometimes the two digit maximum gust in knots
- dSM is the visibility in statute miles. This is somewhat tricky in that d can be any of 0-10 but can also be 1/4, 1/2, 3/4, 1 1/4, 1 1/2, 1 3/4, 2 1/2
- [M]dd/[M]dd is the temperature in celsius and dewpoint in celsius. M means negative
- Adddd is the four-digit altimeter reading
- RMK notes that the remarks are beginning
- SLPddd notes the three-digit sea-level pressure
- Tdddddddd notes the four digit temperature in Celsius and the four digit dewpoint in celsius. If it begins with 1 it is negative. The fourth digit is the decimal. It will always be a Celsius reading that best corresponds to integer degrees of Fahrenheit
Example code includes:
metAll <- metarKLNK %>%
pull(metar)
# Create a search string for METAR
valMet <- "54Z.*?(VRB|\\d{3})(\\d{2})(G\\d{2})?KT(.*?)(\\d{1,2}SM).*?\\s(M?\\d{2})/(M?\\d{2}).*?(A\\d{4}).*?RMK.*?(SLP\\d{3}).*?(T\\d{8})"
# Find the number of matching elements
str_detect(metAll, pattern=valMet) %>% table()## .
## FALSE TRUE
## 23 8790# The strings that do not match have errors in the raw data (typically, missing wind speed)
metAll[!str_detect(metAll, pattern=valMet)]## [1] "KLNK 291354Z 10SM CLR M01/M03 A2976 RMK AO2 SLP088 T10061028"
## [2] "KLNK 012154Z 10SM CLR 01/M08 A3018 RMK AO2 SLP234 T00061083"
## [3] "KLNK 201754Z 10SM CLR 06/M07 A3041 RMK AO2 SLP308 T00611067 10061 21039 58013"
## [4] "KLNK 221654Z 19007KT CLR 15/03 A2956 RMK AO2 SLP006 T01500033 $"
## [5] "KLNK 221754Z 10SM CLR 16/03 A2955 RMK AO2 SLP000 T01610033 10161 20106 58006 $"
## [6] "KLNK 221854Z 10SM CLR 17/03 A2954 RMK AO2 SLP000 T01670033 $"
## [7] "KLNK 050254Z 10SM CLR 14/03 A3004 RMK AO2 SLP169 T01390033 53007"
## [8] "KLNK 181754Z 10SM OVC075 21/04 A3023 RMK AO2 SLP234 T02110039 10217 20072 58011"
## [9] "KLNK 011454Z 21/10 A3008 RMK AO2 SLP178 T02110100 51011 $"
## [10] "KLNK 011554Z 32007KT CLR 22/09 A3008 RMK AO2 SLP179 T02170089 $"
## [11] "KLNK 152254Z 10SM CLR 38/11 A2978 RMK AO2 SLP070 T03780106"
## [12] "KLNK 181954Z 10SM SCT045 31/20 A3017 RMK AO2 SLP205 T03060200"
## [13] "KLNK 261554Z 05006KT 27/18 A3013 RMK AO2 SLP190 T02670178 RVRNO $"
## [14] "KLNK 261654Z 10SM CLR 29/18 A3014 RMK AO2 SLP192 T02890183 $"
## [15] "KLNK 261754Z 10SM CLR 31/18 A3012 RMK AO2 SLP188 T03060178 10306 20222"
## [16] "KLNK 271754Z 10SM CLR 33/18 A3015 RMK AO2 SLP198 T03330183 10333 20222 55001"
## [17] "KLNK 251754Z 10SM CLR 29/17 A3013 RMK AO2 SLP192 T02890167 10289 20194 58010"
## [18] "KLNK 261854Z 10SM CLR 30/18 A3006 RMK AO2 SLP166 T03000183"
## [19] "KLNK 211854Z 10SM CLR 27/10 A3007 RMK AO2 SLP174 T02670100"
## [20] "KLNK 201454Z 10SM CLR 24/21 A3008 RMK AO2 SLP178 T02390206 56004 $"
## [21] "KLNK 201554Z 15014G19KT CLR 27/22 A3007 RMK AO2 SLP174 T02670217 $"
## [22] "KLNK 051854Z 10SM CLR 22/07 A3027 RMK AO2 SLP244 T02170067"
## [23] "KLNK 170754Z AUTO 10SM CLR 08/05 A2950 RMK AO2 SLP984 T00830050"# A matrix of string matches can be obtained
mtxParse <- str_match(metAll, pattern=valMet)
head(mtxParse)## [,1]
## [1,] "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067"
## [2,] "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067"
## [3,] "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067"
## [4,] "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061"
## [5,] "54Z 31005KT 10SM SCT021 OVC027 M03/M07 A3033 RMK AO2 SLP286 T10281067"
## [6,] "54Z AUTO 01009KT 10SM OVC027 M06/M10 A3033 RMK AO2 SLP289 T10611100"
## [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
## [1,] "300" "05" NA " " "10SM" "M03" "M07" "A3029" "SLP275" "T10281067"
## [2,] "000" "00" NA " " "10SM" "M03" "M07" "A3030" "SLP277" "T10331067"
## [3,] "000" "00" NA " " "10SM" "M03" "M07" "A3030" "SLP277" "T10281067"
## [4,] "280" "03" NA " " "10SM" "M03" "M06" "A3031" "SLP281" "T10281061"
## [5,] "310" "05" NA " " "10SM" "M03" "M07" "A3033" "SLP286" "T10281067"
## [6,] "010" "09" NA " " "10SM" "M06" "M10" "A3033" "SLP289" "T10611100"# Create a data frame
dfParse <- data.frame(mtxParse, stringsAsFactors=FALSE)
names(dfParse) <- c("METAR", "WindDir", "WindSpeed", "WindGust", "Dummy", "Visibility",
"TempC", "DewC", "Altimeter", "SLP", "FahrC"
)
dfParse <- tibble::as_tibble(dfParse)
str(dfParse)## Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 11 variables:
## $ METAR : chr "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ WindDir : chr "300" "000" "000" "280" ...
## $ WindSpeed : chr "05" "00" "00" "03" ...
## $ WindGust : chr NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: chr "10SM" "10SM" "10SM" "10SM" ...
## $ TempC : chr "M03" "M03" "M03" "M03" ...
## $ DewC : chr "M07" "M07" "M07" "M06" ...
## $ Altimeter : chr "A3029" "A3030" "A3030" "A3031" ...
## $ SLP : chr "SLP275" "SLP277" "SLP277" "SLP281" ...
## $ FahrC : chr "T10281067" "T10331067" "T10281067" "T10281061" ...# Convert to numeric where appropriate
dfParse <- dfParse %>%
mutate(WindSpeed = as.integer(WindSpeed),
WindGust = as.numeric(WindGust),
Visibility = as.numeric(str_replace(Visibility, "SM", "")),
TempC = as.integer(str_replace(TempC, "M", "-")),
DewC = as.integer(str_replace(DewC, "M", "-")),
Altimeter = as.integer(str_replace(Altimeter, "A", "")),
SLP = as.integer(str_replace(SLP, "SLP", "")),
TempF = 32 + 1.8 * as.integer(str_replace(str_sub(FahrC, 2, 5), pattern="^1", "-"))/10,
DewF = 32 + 1.8 * as.integer(str_replace(str_sub(FahrC, 6, 9), pattern="^1", "-"))/10
)## Warning: NAs introduced by coercion# Investigate the data
set.seed(2003211416)
str(dfParse)## Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 13 variables:
## $ METAR : chr "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ WindDir : chr "300" "000" "000" "280" ...
## $ WindSpeed : int 5 0 0 3 5 9 0 3 0 0 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 10 10 10 10 10 10 10 10 10 10 ...
## $ TempC : int -3 -3 -3 -3 -3 -6 -7 -8 -10 -9 ...
## $ DewC : int -7 -7 -7 -6 -7 -10 -11 -11 -12 -12 ...
## $ Altimeter : int 3029 3030 3030 3031 3033 3033 3034 3034 3034 3035 ...
## $ SLP : int 275 277 277 281 286 289 290 291 295 295 ...
## $ FahrC : chr "T10281067" "T10331067" "T10281067" "T10281061" ...
## $ TempF : num 27 26.1 27 27 27 ...
## $ DewF : num 19.9 19.9 19.9 21 19.9 ...head(dfParse)## # A tibble: 6 x 13
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 54Z ~ 300 5 NA " " 10 -3 -7 3029 275
## 2 54Z ~ 000 0 NA " " 10 -3 -7 3030 277
## 3 54Z ~ 000 0 NA " " 10 -3 -7 3030 277
## 4 54Z ~ 280 3 NA " " 10 -3 -6 3031 281
## 5 54Z ~ 310 5 NA " " 10 -3 -7 3033 286
## 6 54Z ~ 010 9 NA " " 10 -6 -10 3033 289
## # ... with 3 more variables: FahrC <chr>, TempF <dbl>, DewF <dbl>tail(dfParse)## # A tibble: 6 x 13
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 54Z ~ 160 6 NA " " 10 2 -4 2993 147
## 2 54Z ~ VRB 4 NA " " 10 4 -4 2990 139
## 3 54Z ~ 130 7 NA " " 10 4 -7 2989 134
## 4 54Z ~ 110 7 NA " " 10 4 -7 2988 130
## 5 54Z ~ 100 10 NA " " 10 3 -5 2986 125
## 6 54Z ~ 100 10 NA " " 10 3 -6 2986 123
## # ... with 3 more variables: FahrC <chr>, TempF <dbl>, DewF <dbl>dfParse %>%
sample_n(20)## # A tibble: 20 x 13
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 54Z ~ 360 7 NA " " 10 21 15 2995 130
## 2 54Z ~ 210 13 NA " " 10 31 4 2988 108
## 3 54Z ~ 340 11 NA " " 10 -7 -13 3025 259
## 4 54Z ~ 240 9 NA " " 10 13 8 2961 18
## 5 54Z ~ 310 6 NA " " 10 -7 -19 3044 329
## 6 54Z ~ 040 7 NA " " 10 26 14 3010 179
## 7 54Z ~ 170 17 NA " " 10 31 22 2983 90
## 8 54Z ~ 160 7 NA " " 10 -1 -4 3002 182
## 9 54Z ~ 310 9 NA " " 10 13 9 3021 226
## 10 54Z ~ 110 10 NA " " 10 16 16 2990 116
## 11 54Z ~ 140 5 NA " " 10 19 18 3013 199
## 12 54Z ~ 110 6 NA " " 10 20 4 3012 197
## 13 54Z ~ 220 4 NA " " 10 3 -4 3042 311
## 14 54Z ~ 350 3 NA " " 8 9 8 3011 194
## 15 54Z ~ 080 8 NA " " 10 -12 -18 3046 340
## 16 54Z ~ 320 7 NA " " 10 16 11 2998 144
## 17 54Z ~ 330 6 NA " " 10 9 5 3016 214
## 18 54Z ~ 340 13 NA " " 3 0 -1 2979 99
## 19 54Z ~ 190 5 NA " " 10 3 -4 3024 251
## 20 54Z ~ 320 18 NA " " 10 24 12 2999 147
## # ... with 3 more variables: FahrC <chr>, TempF <dbl>, DewF <dbl># Check for NA values
colSums(is.na(dfParse))## METAR WindDir WindSpeed WindGust Dummy Visibility TempC
## 23 23 23 8813 23 23 23
## DewC Altimeter SLP FahrC TempF DewF
## 23 23 23 23 23 23# Plot of counts by key metric
keyMetric <- c("WindDir", "WindSpeed", "WindGust", "Visibility", "TempC",
"DewC", "Altimeter", "SLP", "TempF", "DewF"
)
for (x in keyMetric) {
p <- dfParse %>%
group_by_at(vars(all_of(x))) %>%
summarize(n=n()) %>%
ggplot(aes_string(x=x, y="n")) +
geom_col() +
labs(title=x, y="Count")
print(p)
}
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(diff(sort(x))): no non-missing arguments to min; returning Inf## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
# There are three obvious issues
# Visibility is not correctly picked up when there is a / such as 1/2 SM
# Wind gusts are never picked up
# Sea Level Pressure is missing a digit
# Correct for visibility
# Areas that have \\d \\d/\\dSM
sm1 <- which(str_detect(metAll, pattern=" \\d/\\dSM"))
sm2 <- which(str_detect(metAll, pattern=" \\d \\d/\\dSM"))
valSM1 <- str_match(metAll, pattern="\\d/\\dSM")[sm1]
valSM1 <- str_replace(valSM1, "SM", "")
valSM1 <- as.integer(str_sub(valSM1, 1, 1)) / as.integer(str_sub(valSM1, 3, 3))
valSM2 <- str_match(metAll, pattern=" \\d \\d/\\dSM")[sm2]
valSM2 <- as.integer(str_sub(valSM2, 2, 2))
dfParse[sm1, "Visibility"] <- valSM1
dfParse[sm2, "Visibility"] <- dfParse[sm2, "Visibility"] + valSM2
dfParse %>%
count(Visibility)## # A tibble: 18 x 2
## Visibility n
## <dbl> <int>
## 1 0.25 20
## 2 0.5 16
## 3 0.75 15
## 4 1 19
## 5 1.25 13
## 6 1.5 17
## 7 1.75 21
## 8 2 50
## 9 2.5 38
## 10 3 70
## 11 4 108
## 12 5 108
## 13 6 146
## 14 7 189
## 15 8 221
## 16 9 290
## 17 10 7449
## 18 NA 23# Correct for wind gusts
gustCheck <- which(str_detect(metAll, pattern="\\d{5}G\\d{2}KT"))
valGust <- str_match(metAll, pattern="\\d{5}G\\d{2}KT")[gustCheck]
valGust <- as.integer(str_sub(valGust, 7, 8))
dfParse[gustCheck, "WindGust"] <- valGust
dfParse %>%
count(WindGust) %>%
as.data.frame## WindGust n
## 1 14 4
## 2 15 11
## 3 16 11
## 4 17 16
## 5 18 29
## 6 19 59
## 7 20 69
## 8 21 87
## 9 22 83
## 10 23 107
## 11 24 101
## 12 25 83
## 13 26 62
## 14 27 61
## 15 28 61
## 16 29 37
## 17 30 28
## 18 31 24
## 19 32 18
## 20 33 13
## 21 34 12
## 22 35 14
## 23 36 6
## 24 37 6
## 25 38 10
## 26 39 11
## 27 40 2
## 28 41 3
## 29 42 1
## 30 43 2
## 31 45 2
## 32 NA 7780# Correct for SLP
dfParse <- dfParse %>%
mutate(modSLP=ifelse(dfParse$SLP < 500, 1000 + dfParse$SLP/10, 900 + dfParse$SLP/10))
dfParse %>%
group_by(SLP, modSLP) %>%
summarize(n=n()) %>%
ggplot(aes(x=SLP, y=modSLP, size=n)) +
geom_point(alpha=0.3)## Warning: Removed 1 rows containing missing values (geom_point).
# Check updated plots
keyMetric <- c("WindGust", "Visibility", "modSLP")
for (x in keyMetric) {
p <- dfParse %>%
group_by_at(vars(all_of(x))) %>%
summarize(n=n()) %>%
ggplot(aes_string(x=x, y="n")) +
geom_col() +
labs(title=x, y="Count")
print(p)
}## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
Example #14: Relationships Between METAR Variables
Many of the METAR variables are correlated/associated to one another.
Example code includes:
# Define key numeric variables
coreNum <- c("TempC", "TempF", "DewC", "DewF", "Altimeter", "modSLP", "WindSpeed", "Visibility")
# Add the date back to the file (should edit the above instead)
dfParse <- dfParse %>%
mutate(month=lubridate::month(metarKLNK$valid))
str(dfParse)## Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 15 variables:
## $ METAR : chr "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ WindDir : chr "300" "000" "000" "280" ...
## $ WindSpeed : int 5 0 0 3 5 9 0 3 0 0 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 10 10 10 10 10 10 10 10 10 10 ...
## $ TempC : int -3 -3 -3 -3 -3 -6 -7 -8 -10 -9 ...
## $ DewC : int -7 -7 -7 -6 -7 -10 -11 -11 -12 -12 ...
## $ Altimeter : int 3029 3030 3030 3031 3033 3033 3034 3034 3034 3035 ...
## $ SLP : int 275 277 277 281 286 289 290 291 295 295 ...
## $ FahrC : chr "T10281067" "T10331067" "T10281067" "T10281061" ...
## $ TempF : num 27 26.1 27 27 27 ...
## $ DewF : num 19.9 19.9 19.9 21 19.9 ...
## $ modSLP : num 1028 1028 1028 1028 1029 ...
## $ month : num 12 12 12 12 12 12 12 12 12 12 ...# Keep only complete cases and find correlations
mtxCorr <- dfParse %>%
mutate(month=lubridate::month(metarKLNK$valid)) %>%
select_at(vars(all_of(coreNum))) %>%
filter(complete.cases(.)) %>%
cor()
# Print the correlations and show a heatmap
mtxCorr %>%
round(2)## TempC TempF DewC DewF Altimeter modSLP WindSpeed Visibility
## TempC 1.00 1.00 0.91 0.91 -0.38 -0.48 0.13 0.19
## TempF 1.00 1.00 0.91 0.91 -0.38 -0.48 0.13 0.19
## DewC 0.91 0.91 1.00 1.00 -0.38 -0.48 -0.01 0.07
## DewF 0.91 0.91 1.00 1.00 -0.38 -0.48 -0.01 0.07
## Altimeter -0.38 -0.38 -0.38 -0.38 1.00 0.99 -0.26 0.07
## modSLP -0.48 -0.48 -0.48 -0.48 0.99 1.00 -0.25 0.04
## WindSpeed 0.13 0.13 -0.01 -0.01 -0.26 -0.25 1.00 -0.01
## Visibility 0.19 0.19 0.07 0.07 0.07 0.04 -0.01 1.00corrplot::corrplot(mtxCorr, method="color", title="Lincoln, NE Hourly Weather Correlations (2016)")
# Create a function for plotting two variables against each other
plotNumCor <- function(var1, var2, title=NULL) {
if (is.null(title))
{ title <- paste0("Lincoln, NE (2016) Hourly Correlations of ", var1, " and ", var2) }
p <- dfParse %>%
group_by_at(vars(all_of(c(var1, var2)))) %>%
summarize(n=n()) %>%
ggplot(aes_string(x=var1, y=var2)) +
geom_point(alpha=0.5, aes_string(size="n")) +
geom_smooth(method="lm", aes_string(weight="n")) +
labs(x=var1, y=var2, title=title)
print(p)
}
# The three linear or almost linear relationships
plotNumCor("TempC", "TempF")## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
plotNumCor("DewC", "DewF")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
plotNumCor("Altimeter", "modSLP")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
# Strongly and positively related
plotNumCor("TempF", "DewF")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
# Moderately negatively correlated
plotNumCor("TempF", "Altimeter")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
plotNumCor("TempF", "modSLP")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
plotNumCor("Altimeter", "WindSpeed")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
# Predict modSLP from Altimeter
lmSLP1 <- lm(modSLP ~ Altimeter, data=dfParse)
lmSLP2 <- lm(modSLP ~ Altimeter + TempF, data=dfParse)
summary(lmSLP1)##
## Call:
## lm(formula = modSLP ~ Altimeter, data = dfParse)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8890 -0.8172 -0.1578 0.7610 2.7890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.233e+01 1.406e+00 -44.34 <2e-16 ***
## Altimeter 3.594e-01 4.683e-04 767.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9691 on 8788 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.9853, Adjusted R-squared: 0.9853
## F-statistic: 5.889e+05 on 1 and 8788 DF, p-value: < 2.2e-16summary(lmSLP2)##
## Call:
## lm(formula = modSLP ~ Altimeter + TempF, data = dfParse)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.24130 -0.26737 0.00686 0.25214 1.23326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.002e+01 5.615e-01 -17.84 <2e-16 ***
## Altimeter 3.428e-01 1.857e-04 1845.63 <2e-16 ***
## TempF -4.559e-02 1.921e-04 -237.31 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3561 on 8787 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.998, Adjusted R-squared: 0.998
## F-statistic: 2.209e+06 on 2 and 8787 DF, p-value: < 2.2e-16# Plot predictions vs. actual (model 1)
dfParse %>%
filter(!is.na(modSLP)) %>%
mutate(pred1=predict(lmSLP1)) %>%
count(modSLP, pred1) %>%
ggplot(aes(x=modSLP, y=pred1)) +
geom_point(alpha=0.25, aes(size=n)) +
geom_smooth(method="lm", aes(weight=n)) +
labs(title="Predicted vs. Actual Sea Level Pressure - Altitude Only as Predictor",
subtitle="Lincoln, NE (2016) Hourly METAR", x="Sea Level Pressure", y="Predicted"
)
# Plot predictions vs. actual (model 2)
dfParse %>%
filter(!is.na(modSLP)) %>%
mutate(pred2=predict(lmSLP2)) %>%
count(modSLP, pred2) %>%
ggplot(aes(x=modSLP, y=pred2)) +
geom_point(alpha=0.25, aes(size=n)) +
geom_smooth(method="lm", aes(weight=n)) +
labs(title="Predicted vs. Actual Sea Level Pressure - Altitude and Temperature as Predictor",
subtitle="Lincoln, NE (2016) Hourly METAR", x="Sea Level Pressure", y="Predicted"
)
Example #15: Extracting Cloud Data from METAR
Cloud data is also included in the METAR, with the type of clouds being described as:
- CLR - there are no clouds below 12,000 feet
- VVddd - there is a vertical visibility of ddd hundred feet (cannot tell where the clouds are above that)
- FEWddd - there are clouds with bases at ddd feet, and they obscure 25% or less of the sky
- SCTddd - there are clouds with bases at ddd feet, and they obscure 25%-50% of the sky
- BKNddd - there are clouds with bases at ddd feet, and they obscure 50%-99% of the sky
- OVCddd - there is a full overcast with base at ddd feet
The ceiling is considered the lowest height that is measured as any of OVC, BKN, or VV.
Example code includes:
# Extract the CLR records
mtxCLR <- str_extract_all(metarKLNK$metar, pattern=" CLR ", simplify=TRUE)
if (dim(mtxCLR)[[2]] != 1) { stop("Extracted 2+ CLR from some METAR; investigate") }
isCLR <- ifelse(mtxCLR[, 1] == "", 0, 1)
# Extract the VV records
mtxVV <- str_extract_all(metarKLNK$metar, pattern="VV(\\d{3})", simplify=TRUE)
if (dim(mtxVV)[[2]] != 1) { stop("Extracted 2+ VV from some METAR; investigate") }
isVV <- ifelse(mtxVV[, 1] == "", 0, 1)
htVV <- ifelse(mtxVV[, 1] == "", NA, as.integer(str_replace(mtxVV[, 1], "VV", ""))*100)
# Extract the FEW records
mtxFEW <- str_extract_all(metarKLNK$metar, pattern="FEW(\\d{3})", simplify=TRUE)
numFEW <- apply(mtxFEW, 1, FUN=function(x) { sum((x!=""))} )
# Extract the SCT records
mtxSCT <- str_extract_all(metarKLNK$metar, pattern="SCT(\\d{3})", simplify=TRUE)
numSCT <- apply(mtxSCT, 1, FUN=function(x) { sum((x!=""))} )
# Extract the BKN records
mtxBKN <- str_extract_all(metarKLNK$metar, pattern="BKN(\\d{3})", simplify=TRUE)
numBKN <- apply(mtxBKN, 1, FUN=function(x) { sum((x!=""))} )
# Extract the OVC records
mtxOVC <- str_extract_all(metarKLNK$metar, pattern="OVC(\\d{3})", simplify=TRUE)
numOVC <- apply(mtxOVC, 1, FUN=function(x) { sum((x!=""))} )
# Summarize as a data frame
tblClouds <- tibble::tibble(isCLR=isCLR, isVV=isVV, htVV=htVV, numFEW=numFEW,
numSCT=numSCT, numBKN=numBKN, numOVC=numOVC
)
# Get the counts
# As expected, if isCLR then nothing else, and if isVV then nothing else
tblClouds %>%
count(isCLR, isVV, numFEW, numSCT, numBKN, numOVC) %>%
as.data.frame()## isCLR isVV numFEW numSCT numBKN numOVC n
## 1 0 0 0 0 0 0 6
## 2 0 0 0 0 0 1 1389
## 3 0 0 0 0 1 0 307
## 4 0 0 0 0 1 1 250
## 5 0 0 0 0 2 0 50
## 6 0 0 0 0 2 1 45
## 7 0 0 0 0 3 0 8
## 8 0 0 0 1 0 0 230
## 9 0 0 0 1 0 1 73
## 10 0 0 0 1 1 0 52
## 11 0 0 0 1 1 1 42
## 12 0 0 0 1 2 0 17
## 13 0 0 0 2 0 0 16
## 14 0 0 0 2 0 1 9
## 15 0 0 0 2 1 0 6
## 16 0 0 1 0 0 0 380
## 17 0 0 1 0 0 1 90
## 18 0 0 1 0 1 0 46
## 19 0 0 1 0 1 1 62
## 20 0 0 1 0 2 0 9
## 21 0 0 1 1 0 0 39
## 22 0 0 1 1 0 1 24
## 23 0 0 1 1 1 0 28
## 24 0 0 1 2 0 0 10
## 25 0 0 2 0 0 0 24
## 26 0 0 2 0 0 1 6
## 27 0 0 2 0 1 0 5
## 28 0 0 2 1 0 0 3
## 29 0 0 3 0 0 0 2
## 30 0 1 0 0 0 0 33
## 31 1 0 0 0 0 0 5552# Investigate the problem data
metarKLNK$metar[rowSums(tblClouds, na.rm=TRUE)==0]## [1] "KLNK 301854Z 17011KT 10SM 30/18 A2993 RMK AO2 SLP124 T03000178 $"
## [2] "KLNK 011454Z 21/10 A3008 RMK AO2 SLP178 T02110100 51011 $"
## [3] "KLNK 261554Z 05006KT 27/18 A3013 RMK AO2 SLP190 T02670178 RVRNO $"
## [4] "KLNK 161554Z 09009KT 10SM 26/19 A3007 RMK AO2 SLP171 T02610194 $"
## [5] "KLNK 211954Z 25008KT 210V270 10SM 27/10 A3005 RMK AO2 SLP170 T02720100 $"
## [6] "KLNK 050854Z 00000KT 1/2SM R36/3000VP6000FT FG 09/09 A2978 RMK AO2 SLP081 T00940094 51008 $"# Get the counts of most obscuration
tblClouds %>%
filter(rowSums(., na.rm=TRUE) > 0) %>%
mutate(wType=factor(case_when(isCLR==1 ~ "CLR", isVV==1 ~ "VV", numOVC > 0 ~ "OVC",
numBKN > 0 ~ "BKN", numSCT > 0 ~ "SCT", numFEW > 0 ~ "FEW",
TRUE ~ "Error"
), levels=c("VV", "OVC", "BKN", "SCT", "FEW", "CLR", "Error")
)
) %>%
ggplot(aes(x=wType, y=..count../sum(..count..))) +
geom_bar() +
labs(title="Highest Obscuration by Cloud - Lincoln, NE (2016)", x="Cloud Type",
y="Proportion of Hourly Measurements"
)
# Integrate the clouds data
mtxCloud <- cbind(mtxVV, mtxOVC, mtxBKN, mtxSCT, mtxFEW, mtxCLR)
# Cycle through to find levels of a given type
ckClouds <- function(cloudType) {
isKey <- which(apply(mtxCloud, 2, FUN=function(x) {sum(str_detect(x, cloudType))}) > 0)
as.integer(str_replace(mtxCloud[, min(isKey)], cloudType, "")) * 100
}
lowOVC <- ckClouds("OVC")
lowVV <- ckClouds("VV")
lowBKN <- ckClouds("BKN")
lowSCT <- ckClouds("SCT")
lowFEW <- ckClouds("FEW")
# Integrate the lowest cloud type by level
lowCloud <- tibble::tibble(lowVV, lowOVC, lowBKN, lowSCT, lowFEW)
lowCloud## # A tibble: 8,813 x 5
## lowVV lowOVC lowBKN lowSCT lowFEW
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NA 2800 NA NA NA
## 2 NA 2700 NA NA NA
## 3 NA 2600 NA NA NA
## 4 NA 2700 NA NA NA
## 5 NA 2700 NA 2100 NA
## 6 NA 2700 NA NA NA
## 7 NA 2700 NA NA NA
## 8 NA 2700 NA NA NA
## 9 NA NA NA NA 2600
## 10 NA NA NA NA NA
## # ... with 8,803 more rows# Get the lowest cloud level
minCloud <- lowCloud
minCloud[is.na(minCloud)] <- 999999
minCloudLevel <- apply(minCloud, 1, FUN=min)
minCeilingLevel <- apply(minCloud[, c("lowVV", "lowOVC", "lowBKN")], 1, FUN=min)
noCloudPct <- mean(minCloudLevel == 999999)
noCeilingPct <- mean(minCeilingLevel == 999999)
# Plot the minimum cloud level (where it exists)
data.frame(minCloudLevel, minCeilingLevel) %>%
filter(minCloudLevel != 999999) %>%
ggplot(aes(x=minCloudLevel)) +
geom_bar(aes(y=..count../sum(..count..))) +
geom_text(aes(x=2500, y=0.04,
label=paste0(round(100*noCloudPct), "% of obs. have no clouds")
)
) +
labs(x="Height [ft]", y="Proportion", title="Minimum Cloud Height (when some clouds exist)",
subtitle="Lincoln, NE (2016)"
)
# Plot the minimum ceiling level (where it exists)
data.frame(minCloudLevel, minCeilingLevel) %>%
filter(minCeilingLevel != 999999) %>%
ggplot(aes(x=minCeilingLevel)) +
geom_bar(aes(y=..count../sum(..count..))) +
geom_text(aes(x=2500, y=0.04,
label=paste0(round(100*noCeilingPct), "% of obs. have no ceiling")
)
) +
labs(x="Height [ft]", y="Proportion", title="Minimum Ceiling Height (when a ceiling exists)",
subtitle="Lincoln, NE (2016)"
)
Example #16: Plotting by factor variables
The month of the year is an interesting data point for plotting against.
Example code includes:
# Integrate the cloud data and convert month to a factor
dfFull <- cbind(dfParse, tblClouds, lowCloud) %>%
mutate(wType=factor(case_when(isCLR==1 ~ "CLR", isVV==1 ~ "VV", numOVC > 0 ~ "OVC",
numBKN > 0 ~ "BKN", numSCT > 0 ~ "SCT", numFEW > 0 ~ "FEW",
TRUE ~ "Error"
), levels=c("VV", "OVC", "BKN", "SCT", "FEW", "CLR", "Error")
),
month=factor(month, levels=1:12, labels=month.abb)
)
dfFull <- tibble::as_tibble(dfFull)
str(dfFull)## Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 28 variables:
## $ METAR : chr "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ WindDir : chr "300" "000" "000" "280" ...
## $ WindSpeed : int 5 0 0 3 5 9 0 3 0 0 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 10 10 10 10 10 10 10 10 10 10 ...
## $ TempC : int -3 -3 -3 -3 -3 -6 -7 -8 -10 -9 ...
## $ DewC : int -7 -7 -7 -6 -7 -10 -11 -11 -12 -12 ...
## $ Altimeter : int 3029 3030 3030 3031 3033 3033 3034 3034 3034 3035 ...
## $ SLP : int 275 277 277 281 286 289 290 291 295 295 ...
## $ FahrC : chr "T10281067" "T10331067" "T10281067" "T10281061" ...
## $ TempF : num 27 26.1 27 27 27 ...
## $ DewF : num 19.9 19.9 19.9 21 19.9 ...
## $ modSLP : num 1028 1028 1028 1028 1029 ...
## $ month : Factor w/ 12 levels "Jan","Feb","Mar",..: 12 12 12 12 12 12 12 12 12 12 ...
## $ isCLR : num 0 0 0 0 0 0 0 0 0 1 ...
## $ isVV : num 0 0 0 0 0 0 0 0 0 0 ...
## $ htVV : num NA NA NA NA NA NA NA NA NA NA ...
## $ numFEW : int 0 0 0 0 0 0 0 0 1 0 ...
## $ numSCT : int 0 0 0 0 1 0 0 0 0 0 ...
## $ numBKN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ numOVC : int 1 1 1 1 1 1 1 1 0 0 ...
## $ lowVV : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowOVC : num 2800 2700 2600 2700 2700 2700 2700 2700 NA NA ...
## $ lowBKN : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowSCT : num NA NA NA NA 2100 NA NA NA NA NA ...
## $ lowFEW : num NA NA NA NA NA NA NA NA 2600 NA ...
## $ wType : Factor w/ 7 levels "VV","OVC","BKN",..: 2 2 2 2 2 2 2 2 5 6 ...# Run the boxplot of a factor against a numeric variable
plotFactorNumeric <- function(fctVar, numVar, title=NULL) {
if (is.null(title)) { title <- paste0("Lincoln, NE (2016) Hourly Weather - ", numVar, " vs. ", fctVar) }
p <- dfFull %>%
filter(!is.na(get(fctVar)), !is.na(get(numVar))) %>%
ggplot(aes_string(x=fctVar, y=numVar)) +
geom_boxplot(fill="lightblue") +
labs(title=title)
print(p)
}
# Run for all of the key variables against wind speed and cloud type
keyVar <- c("WindSpeed", "Visibility", "Altimeter", "TempF", "DewF")
for (var in keyVar) { plotFactorNumeric("month", var) }
for (var in keyVar) { plotFactorNumeric("wType", var) }
# Create stacked bars for cloud type by month
dfFull %>%
filter(!is.na(wType), wType!="Error") %>%
ggplot(aes(x=month, fill=wType)) +
geom_bar(position="fill") +
labs(title="Lincoln, NE (2016)", x="", y="Proportion of Month")
Example #17: Functional Form - METAR download and initial wind processing
Example 12 can be converted to functional form so that the process can be applied to other reporting stations and time periods.
Example code includes:
# Function to make an initial read of the data, filter to METAR records, and check date-times
readMETAR <- function(fileName="./RInputFiles/metar_klnk_2016.txt", timeZ="54Z",
expMin=as.POSIXct("2015-12-31 00:54:00", tz="UTC"), expDays=365
) {
# Load METAR data
initRead <- readr::read_csv(fileName, na=c("", "NA", "M"))
str(initRead, give.attr=FALSE)
# Filter to only data that ends with times ending in 54Z
filterRead <- initRead %>%
filter(str_detect(metar, timeZ))
dim(filterRead)
# Check that the dates and times included are as expected
expDate <- expMin + lubridate::hours(0:(24*expDays - 1))
# Observations expected but not recorded
cat("\n*** OBSERVATIONS EXPECTED BUT NOT RECORDED ***\n")
print(as.POSIXct(setdiff(expDate, filterRead$valid), origin="1970-01-01", tz="UTC"))
# Observations recorded but not expected
cat("\n*** OBSERVATIONS RECORDED BUT NOT EXPECTED ***\n")
print(as.POSIXct(setdiff(filterRead$valid, expDate), origin="1970-01-01", tz="UTC"))
# Confirmation of uniqueness
cat("\n*** Are the extracted records unique? ***\n")
print(length(unique(filterRead$valid)) == length(filterRead$valid))
cat("\n")
# Return the dataset as a tibble
tibble::as_tibble(filterRead)
}
funcMETAR <- readMETAR(expDays=368)## Parsed with column specification:
## cols(
## .default = col_double(),
## station = col_character(),
## valid = col_datetime(format = ""),
## p01i = col_character(),
## skyc1 = col_character(),
## skyc2 = col_character(),
## skyc3 = col_character(),
## skyc4 = col_logical(),
## skyl4 = col_logical(),
## wxcodes = col_character(),
## ice_accretion_1hr = col_character(),
## ice_accretion_3hr = col_character(),
## ice_accretion_6hr = col_character(),
## peak_wind_time = col_datetime(format = ""),
## metar = col_character()
## )## See spec(...) for full column specifications.## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 10594 obs. of 29 variables:
## $ station : chr "LNK" "LNK" "LNK" "LNK" ...
## $ valid : POSIXct, format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## $ tmpf : num 27 26.1 27 27 27 ...
## $ dwpf : num 19.9 19.9 19.9 21 19.9 ...
## $ relh : num 74.5 77.3 74.5 78 74.5 ...
## $ drct : num 300 0 0 280 310 10 0 10 20 0 ...
## $ sknt : num 5 0 0 3 5 9 0 3 3 0 ...
## $ p01i : chr "0.00" "0.00" "0.00" "0.00" ...
## $ alti : num 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num 1028 1028 1028 1028 1029 ...
## $ vsby : num 10 10 10 10 10 10 10 10 10 10 ...
## $ gust : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr "OVC" "OVC" "OVC" "OVC" ...
## $ skyc2 : chr NA NA NA NA ...
## $ skyc3 : chr NA NA NA NA ...
## $ skyc4 : logi NA NA NA NA NA NA ...
## $ skyl1 : num 2800 2700 2600 2700 2100 2700 2700 2700 2600 2600 ...
## $ skyl2 : num NA NA NA NA 2700 NA NA NA NA NA ...
## $ skyl3 : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyl4 : logi NA NA NA NA NA NA ...
## $ wxcodes : chr NA NA NA NA ...
## $ ice_accretion_1hr: chr NA NA NA NA ...
## $ ice_accretion_3hr: chr NA NA NA NA ...
## $ ice_accretion_6hr: chr NA NA NA NA ...
## $ peak_wind_gust : num NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_drct : num NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_time : POSIXct, format: NA NA ...
## $ feel : num 20.4 26.1 27 22.9 20.4 ...
## $ metar : chr "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
##
## *** OBSERVATIONS EXPECTED BUT NOT RECORDED ***
## [1] "2016-01-19 11:54:00 UTC" "2016-05-06 11:54:00 UTC"
## [3] "2016-05-06 12:54:00 UTC" "2016-06-17 23:54:00 UTC"
## [5] "2016-06-18 00:54:00 UTC" "2016-06-18 07:54:00 UTC"
## [7] "2016-07-02 15:54:00 UTC" "2016-07-13 14:54:00 UTC"
## [9] "2016-07-13 15:54:00 UTC" "2016-07-13 16:54:00 UTC"
## [11] "2016-07-13 17:54:00 UTC" "2016-07-30 13:54:00 UTC"
## [13] "2016-08-02 07:54:00 UTC" "2016-08-05 07:54:00 UTC"
## [15] "2016-08-29 21:54:00 UTC" "2016-09-15 16:54:00 UTC"
## [17] "2016-09-16 05:54:00 UTC" "2016-11-21 00:54:00 UTC"
## [19] "2016-12-03 08:54:00 UTC"
##
## *** OBSERVATIONS RECORDED BUT NOT EXPECTED ***
## POSIXct of length 0
##
## *** Are the extracted records unique? ***
## [1] TRUEfuncMETAR## # A tibble: 8,813 x 29
## station valid tmpf dwpf relh drct sknt p01i alti mslp
## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 LNK 2015-12-31 00:54:00 27.0 19.9 74.5 300 5 0.00 30.3 1028.
## 2 LNK 2015-12-31 01:54:00 26.1 19.9 77.3 0 0 0.00 30.3 1028.
## 3 LNK 2015-12-31 02:54:00 27.0 19.9 74.5 0 0 0.00 30.3 1028.
## 4 LNK 2015-12-31 03:54:00 27.0 21.0 78 280 3 0.00 30.3 1028.
## 5 LNK 2015-12-31 04:54:00 27.0 19.9 74.5 310 5 0.00 30.3 1029.
## 6 LNK 2015-12-31 05:54:00 21.0 14 73.9 10 9 0.00 30.3 1029.
## 7 LNK 2015-12-31 06:54:00 19.0 12.0 73.7 0 0 0.00 30.3 1029
## 8 LNK 2015-12-31 07:54:00 18.0 12.0 77.2 10 3 0.00 30.3 1029.
## 9 LNK 2015-12-31 08:54:00 14 10.0 83.9 0 0 0.00 30.3 1030.
## 10 LNK 2015-12-31 09:54:00 16.0 10.9 80.1 0 0 0.00 30.4 1030.
## # ... with 8,803 more rows, and 19 more variables: vsby <dbl>, gust <dbl>,
## # skyc1 <chr>, skyc2 <chr>, skyc3 <chr>, skyc4 <lgl>, skyl1 <dbl>,
## # skyl2 <dbl>, skyl3 <dbl>, skyl4 <lgl>, wxcodes <chr>,
## # ice_accretion_1hr <chr>, ice_accretion_3hr <chr>, ice_accretion_6hr <chr>,
## # peak_wind_gust <dbl>, peak_wind_drct <dbl>, peak_wind_time <dttm>,
## # feel <dbl>, metar <chr># Extract wind speeds and direction
# The general wind format is dddssGssKT where ddd is the direction (VRB meaning variable), the main ss is the speed, and the Gss is the gust speed (optional and not always displayed)
extractWind <- function(met) {
mtxWind <- met %>%
pull(metar) %>%
str_match(pattern="(\\d{3}|VRB)(\\d{2})(G\\d{2})?KT")
cat("\n*** First 6 winds and parsing ***\n")
print(head(mtxWind))
cat("\n*** Table of WIND DIRECTION ***\n")
print(table(mtxWind[, 2], useNA="ifany"))
cat("\n*** Table of WIND SPEED ***\n")
print(table(mtxWind[, 3], useNA="ifany"))
cat("\n*** Table of WIND GUST ***\n")
print(table(mtxWind[, 4], useNA="ifany"))
# Verify that winds not captured are in fact missing from the METAR
cat("\n *** WIND DATA WAS NOT CAPTURED FROM: *** \n")
print(met[which(is.na(mtxWind[, 2])), "metar"])
cat("\n")
met %>%
mutate(dirW=mtxWind[, 2],
spdW=as.numeric(mtxWind[, 3]),
gustW=as.numeric(str_replace(mtxWind[, 4], "G", ""))
)
}
windMETAR <- extractWind(funcMETAR)##
## *** First 6 winds and parsing ***
## [,1] [,2] [,3] [,4]
## [1,] "30005KT" "300" "05" NA
## [2,] "00000KT" "000" "00" NA
## [3,] "00000KT" "000" "00" NA
## [4,] "28003KT" "280" "03" NA
## [5,] "31005KT" "310" "05" NA
## [6,] "01009KT" "010" "09" NA
##
## *** Table of WIND DIRECTION ***
##
## 000 010 020 030 040 050 060 070 080 090 100 110 120 130 140 150
## 875 269 199 146 135 121 108 95 88 65 102 158 169 245 241 339
## 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310
## 463 565 517 413 284 179 142 96 73 80 105 89 121 141 147 225
## 320 330 340 350 360 VRB <NA>
## 234 303 352 413 383 114 19
##
## *** Table of WIND SPEED ***
##
## 00 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17
## 875 615 704 664 696 651 636 599 536 451 439 371 315 276 235 173
## 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 34
## 156 108 69 62 45 33 23 13 14 9 10 6 6 2 1 1
## <NA>
## 19
##
## *** Table of WIND GUST ***
##
## G14 G15 G16 G17 G18 G19 G20 G21 G22 G23 G24 G25 G26 G27 G28 G29
## 6 11 11 16 30 59 69 87 83 107 101 83 62 61 61 37
## G30 G31 G32 G33 G34 G35 G36 G37 G38 G39 G40 G41 G42 G43 G45 <NA>
## 28 24 18 13 12 14 6 6 10 11 2 3 1 2 2 7777
##
## *** WIND DATA WAS NOT CAPTURED FROM: ***
## # A tibble: 19 x 1
## metar
## <chr>
## 1 KLNK 291354Z 10SM CLR M01/M03 A2976 RMK AO2 SLP088 T10061028
## 2 KLNK 012154Z 10SM CLR 01/M08 A3018 RMK AO2 SLP234 T00061083
## 3 KLNK 201754Z 10SM CLR 06/M07 A3041 RMK AO2 SLP308 T00611067 10061 21039 58013
## 4 KLNK 221754Z 10SM CLR 16/03 A2955 RMK AO2 SLP000 T01610033 10161 20106 58006~
## 5 KLNK 221854Z 10SM CLR 17/03 A2954 RMK AO2 SLP000 T01670033 $
## 6 KLNK 050254Z 10SM CLR 14/03 A3004 RMK AO2 SLP169 T01390033 53007
## 7 KLNK 181754Z 10SM OVC075 21/04 A3023 RMK AO2 SLP234 T02110039 10217 20072 58~
## 8 KLNK 011454Z 21/10 A3008 RMK AO2 SLP178 T02110100 51011 $
## 9 KLNK 152254Z 10SM CLR 38/11 A2978 RMK AO2 SLP070 T03780106
## 10 KLNK 181954Z 10SM SCT045 31/20 A3017 RMK AO2 SLP205 T03060200
## 11 KLNK 261654Z 10SM CLR 29/18 A3014 RMK AO2 SLP192 T02890183 $
## 12 KLNK 261754Z 10SM CLR 31/18 A3012 RMK AO2 SLP188 T03060178 10306 20222
## 13 KLNK 271754Z 10SM CLR 33/18 A3015 RMK AO2 SLP198 T03330183 10333 20222 55001
## 14 KLNK 251754Z 10SM CLR 29/17 A3013 RMK AO2 SLP192 T02890167 10289 20194 58010
## 15 KLNK 261854Z 10SM CLR 30/18 A3006 RMK AO2 SLP166 T03000183
## 16 KLNK 211854Z 10SM CLR 27/10 A3007 RMK AO2 SLP174 T02670100
## 17 KLNK 201454Z 10SM CLR 24/21 A3008 RMK AO2 SLP178 T02390206 56004 $
## 18 KLNK 051854Z 10SM CLR 22/07 A3027 RMK AO2 SLP244 T02170067
## 19 KLNK 170754Z AUTO 10SM CLR 08/05 A2950 RMK AO2 SLP984 T00830050windMETAR## # A tibble: 8,813 x 32
## station valid tmpf dwpf relh drct sknt p01i alti mslp
## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 LNK 2015-12-31 00:54:00 27.0 19.9 74.5 300 5 0.00 30.3 1028.
## 2 LNK 2015-12-31 01:54:00 26.1 19.9 77.3 0 0 0.00 30.3 1028.
## 3 LNK 2015-12-31 02:54:00 27.0 19.9 74.5 0 0 0.00 30.3 1028.
## 4 LNK 2015-12-31 03:54:00 27.0 21.0 78 280 3 0.00 30.3 1028.
## 5 LNK 2015-12-31 04:54:00 27.0 19.9 74.5 310 5 0.00 30.3 1029.
## 6 LNK 2015-12-31 05:54:00 21.0 14 73.9 10 9 0.00 30.3 1029.
## 7 LNK 2015-12-31 06:54:00 19.0 12.0 73.7 0 0 0.00 30.3 1029
## 8 LNK 2015-12-31 07:54:00 18.0 12.0 77.2 10 3 0.00 30.3 1029.
## 9 LNK 2015-12-31 08:54:00 14 10.0 83.9 0 0 0.00 30.3 1030.
## 10 LNK 2015-12-31 09:54:00 16.0 10.9 80.1 0 0 0.00 30.4 1030.
## # ... with 8,803 more rows, and 22 more variables: vsby <dbl>, gust <dbl>,
## # skyc1 <chr>, skyc2 <chr>, skyc3 <chr>, skyc4 <lgl>, skyl1 <dbl>,
## # skyl2 <dbl>, skyl3 <dbl>, skyl4 <lgl>, wxcodes <chr>,
## # ice_accretion_1hr <chr>, ice_accretion_3hr <chr>, ice_accretion_6hr <chr>,
## # peak_wind_gust <dbl>, peak_wind_drct <dbl>, peak_wind_time <dttm>,
## # feel <dbl>, metar <chr>, dirW <chr>, spdW <dbl>, gustW <dbl># Generate basic wind plots
basicWindPlots <- function(met, desc="Lincoln, NE", gran="KLNK METAR (2016)") {
# Plot for the wind direction
wDir <- met %>%
ggplot(aes(x=dirW)) +
geom_bar() +
labs(title=paste0(desc, " Wind Direction"), subtitle=gran,
y="# Hourly Observations", x="Wind Direction"
)
print(wDir)
# Plot for the minimum, average, and maximum wind speed by wind direction
# Wind direction 000 is reserved for 0 KT wind, while VRB is reserved for 3-6 KT variable winds
wSpeedByDir <- met %>%
filter(!is.na(dirW)) %>%
group_by(dirW) %>%
summarize(minWind=min(spdW), meanWind=mean(spdW), maxWind=max(spdW)) %>%
ggplot(aes(x=dirW)) +
geom_point(aes(y=meanWind), color="red", size=2) +
geom_errorbar(aes(ymin=minWind, ymax=maxWind)) +
labs(title=paste0(desc, " Wind Speed (Max, Mean, Min) By Wind Direction"), subtitle=gran,
y="Wind Speed [KT]", x="Wind Direction"
)
print(wSpeedByDir)
# Plot for the wind speed
pctZero <- sum(met$spdW==0, na.rm=TRUE) / length(met$spdW)
wSpeed <- met %>%
ggplot(aes(x=spdW)) +
geom_bar(aes(y=..count../sum(..count..))) +
labs(title=paste0(round(100*pctZero), "% of wind speeds in ", desc, " measure 0 Knots"),
subtitle=gran,
y="% Hourly Observations", x="Wind Speed {KT]"
)
print(wSpeed)
wPolarDirSpeed <- met %>%
filter(!is.na(dirW), dirW != "VRB", dirW != "000") %>%
mutate(dirW=as.numeric(dirW)) %>%
group_by(dirW, spdW) %>%
summarize(n=n()) %>%
ggplot(aes(x=spdW, y=dirW)) +
geom_point(alpha=0.1, aes(size=n)) +
coord_polar(theta="y") +
labs(title=paste0(desc, " Direction vs. Wind Speed"), subtitle=gran, x="Wind Speed [KT]") +
scale_y_continuous(limits=c(0, 360), breaks=c(0, 90, 180, 270, 360)) +
scale_x_continuous(limits=c(0, 30), breaks=c(0, 5, 10, 15, 20, 25, 30)) +
geom_point(aes(x=0, y=0), color="red", size=2)
print(wPolarDirSpeed)
}
basicWindPlots(windMETAR)
## Warning: Removed 19 rows containing non-finite values (stat_count).
## Warning: Removed 4 rows containing missing values (geom_point).
Example #18: Functional Form for Extracting Key Elements from METAR
METAR parsing can also be converted to a functional form. This will need to be modified to be more general, since the codes used for a few things like clouds can vary from station to station.
Example code includes:
# Code for the initial METAR parsing
initialParseMETAR <- function(met, val, labs) {
# Pull the METAR data
metAll <- met %>%
pull(metar)
# Find the number of matching elements
cat("\n*** Tentative Summary of Element Parsing *** \n")
str_detect(metAll, pattern=val) %>%
table() %>%
print()
# The strings that do not match have errors in the raw data (typically, missing wind speed)
cat("\n*** Data Not Matched *** \n")
print(metAll[!str_detect(metAll, pattern=val)])
# A matrix of string matches can be obtained
mtxParse <- str_match(metAll, pattern=val)
cat("\n*** Parsing matrix summary *** \n")
print(dim(mtxParse))
print(head(mtxParse))
# Create a data frame
dfParse <- data.frame(mtxParse, stringsAsFactors=FALSE) %>%
mutate(dtime=met$valid, origMETAR=met$metar)
names(dfParse) <- c(labs, "dtime", "origMETAR")
dfParse <- tibble::as_tibble(dfParse)
cat("\n*** Summary of the parsed data *** \n")
glimpse(dfParse)
dfParse
}
# Create a search string for METAR
valMet <- "54Z.*?(VRB|\\d{3})(\\d{2})(G\\d{2})?KT(.*?)(\\d{1,2}SM).*?\\s(M?\\d{2})/(M?\\d{2}).*?(A\\d{4}).*?RMK.*?(SLP\\d{3}).*?(T\\d{8})"
# Create the names for the search string to parse in to
labsMet <- c("METAR", "WindDir", "WindSpeed", "WindGust", "Dummy", "Visibility",
"TempC", "DewC", "Altimeter", "SLP", "FahrC"
)
# Run the METAR parsing on the raw data
initMETAR <- initialParseMETAR(funcMETAR, val=valMet, labs=labsMet)##
## *** Tentative Summary of Element Parsing ***
## .
## FALSE TRUE
## 23 8790
##
## *** Data Not Matched ***
## [1] "KLNK 291354Z 10SM CLR M01/M03 A2976 RMK AO2 SLP088 T10061028"
## [2] "KLNK 012154Z 10SM CLR 01/M08 A3018 RMK AO2 SLP234 T00061083"
## [3] "KLNK 201754Z 10SM CLR 06/M07 A3041 RMK AO2 SLP308 T00611067 10061 21039 58013"
## [4] "KLNK 221654Z 19007KT CLR 15/03 A2956 RMK AO2 SLP006 T01500033 $"
## [5] "KLNK 221754Z 10SM CLR 16/03 A2955 RMK AO2 SLP000 T01610033 10161 20106 58006 $"
## [6] "KLNK 221854Z 10SM CLR 17/03 A2954 RMK AO2 SLP000 T01670033 $"
## [7] "KLNK 050254Z 10SM CLR 14/03 A3004 RMK AO2 SLP169 T01390033 53007"
## [8] "KLNK 181754Z 10SM OVC075 21/04 A3023 RMK AO2 SLP234 T02110039 10217 20072 58011"
## [9] "KLNK 011454Z 21/10 A3008 RMK AO2 SLP178 T02110100 51011 $"
## [10] "KLNK 011554Z 32007KT CLR 22/09 A3008 RMK AO2 SLP179 T02170089 $"
## [11] "KLNK 152254Z 10SM CLR 38/11 A2978 RMK AO2 SLP070 T03780106"
## [12] "KLNK 181954Z 10SM SCT045 31/20 A3017 RMK AO2 SLP205 T03060200"
## [13] "KLNK 261554Z 05006KT 27/18 A3013 RMK AO2 SLP190 T02670178 RVRNO $"
## [14] "KLNK 261654Z 10SM CLR 29/18 A3014 RMK AO2 SLP192 T02890183 $"
## [15] "KLNK 261754Z 10SM CLR 31/18 A3012 RMK AO2 SLP188 T03060178 10306 20222"
## [16] "KLNK 271754Z 10SM CLR 33/18 A3015 RMK AO2 SLP198 T03330183 10333 20222 55001"
## [17] "KLNK 251754Z 10SM CLR 29/17 A3013 RMK AO2 SLP192 T02890167 10289 20194 58010"
## [18] "KLNK 261854Z 10SM CLR 30/18 A3006 RMK AO2 SLP166 T03000183"
## [19] "KLNK 211854Z 10SM CLR 27/10 A3007 RMK AO2 SLP174 T02670100"
## [20] "KLNK 201454Z 10SM CLR 24/21 A3008 RMK AO2 SLP178 T02390206 56004 $"
## [21] "KLNK 201554Z 15014G19KT CLR 27/22 A3007 RMK AO2 SLP174 T02670217 $"
## [22] "KLNK 051854Z 10SM CLR 22/07 A3027 RMK AO2 SLP244 T02170067"
## [23] "KLNK 170754Z AUTO 10SM CLR 08/05 A2950 RMK AO2 SLP984 T00830050"
##
## *** Parsing matrix summary ***
## [1] 8813 11
## [,1]
## [1,] "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067"
## [2,] "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067"
## [3,] "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067"
## [4,] "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061"
## [5,] "54Z 31005KT 10SM SCT021 OVC027 M03/M07 A3033 RMK AO2 SLP286 T10281067"
## [6,] "54Z AUTO 01009KT 10SM OVC027 M06/M10 A3033 RMK AO2 SLP289 T10611100"
## [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
## [1,] "300" "05" NA " " "10SM" "M03" "M07" "A3029" "SLP275" "T10281067"
## [2,] "000" "00" NA " " "10SM" "M03" "M07" "A3030" "SLP277" "T10331067"
## [3,] "000" "00" NA " " "10SM" "M03" "M07" "A3030" "SLP277" "T10281067"
## [4,] "280" "03" NA " " "10SM" "M03" "M06" "A3031" "SLP281" "T10281061"
## [5,] "310" "05" NA " " "10SM" "M03" "M07" "A3033" "SLP286" "T10281067"
## [6,] "010" "09" NA " " "10SM" "M06" "M10" "A3033" "SLP289" "T10611100"
##
## *** Summary of the parsed data ***
## Observations: 8,813
## Variables: 13
## $ METAR <chr> "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T1...
## $ WindDir <chr> "300", "000", "000", "280", "310", "010", "000", "010", ...
## $ WindSpeed <chr> "05", "00", "00", "03", "05", "09", "00", "03", "00", "0...
## $ WindGust <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ Dummy <chr> " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "...
## $ Visibility <chr> "10SM", "10SM", "10SM", "10SM", "10SM", "10SM", "10SM", ...
## $ TempC <chr> "M03", "M03", "M03", "M03", "M03", "M06", "M07", "M08", ...
## $ DewC <chr> "M07", "M07", "M07", "M06", "M07", "M10", "M11", "M11", ...
## $ Altimeter <chr> "A3029", "A3030", "A3030", "A3031", "A3033", "A3033", "A...
## $ SLP <chr> "SLP275", "SLP277", "SLP277", "SLP281", "SLP286", "SLP28...
## $ FahrC <chr> "T10281067", "T10331067", "T10281067", "T10281061", "T10...
## $ dtime <dttm> 2015-12-31 00:54:00, 2015-12-31 01:54:00, 2015-12-31 02...
## $ origMETAR <chr> "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 ...initMETAR## # A tibble: 8,813 x 13
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 54Z ~ 300 05 <NA> " " 10SM M03 M07 A3029 SLP2~
## 2 54Z ~ 000 00 <NA> " " 10SM M03 M07 A3030 SLP2~
## 3 54Z ~ 000 00 <NA> " " 10SM M03 M07 A3030 SLP2~
## 4 54Z ~ 280 03 <NA> " " 10SM M03 M06 A3031 SLP2~
## 5 54Z ~ 310 05 <NA> " " 10SM M03 M07 A3033 SLP2~
## 6 54Z ~ 010 09 <NA> " " 10SM M06 M10 A3033 SLP2~
## 7 54Z ~ 000 00 <NA> " " 10SM M07 M11 A3034 SLP2~
## 8 54Z ~ 010 03 <NA> " " 10SM M08 M11 A3034 SLP2~
## 9 54Z ~ 000 00 <NA> " " 10SM M10 M12 A3034 SLP2~
## 10 54Z ~ 000 00 <NA> " " 10SM M09 M12 A3035 SLP2~
## # ... with 8,803 more rows, and 3 more variables: FahrC <chr>, dtime <dttm>,
## # origMETAR <chr># Helper function for generating plots by key variables
plotcountsByMetric <- function(df, mets) {
# Plot of counts by key metric
for (x in mets) {
p <- df %>%
group_by_at(vars(all_of(x))) %>%
summarize(n=n()) %>%
ggplot(aes_string(x=x, y="n")) +
geom_col() +
labs(title=x, y="Count")
print(p)
}
}
# Code for the conversion of METAR to meaningful numeric
# Should make this much more general later
convertMETAR <- function(met, metrics, seed=2003211416) {
# Convert to numeric where appropriate
dfParse <- met %>%
mutate(WindSpeed = as.integer(WindSpeed),
WindGust = as.numeric(WindGust),
Visibility = as.numeric(str_replace(Visibility, "SM", "")),
TempC = as.integer(str_replace(TempC, "M", "-")),
DewC = as.integer(str_replace(DewC, "M", "-")),
Altimeter = as.integer(str_replace(Altimeter, "A", "")),
SLP = as.integer(str_replace(SLP, "SLP", "")),
TempF = 32 + 1.8 * as.integer(str_replace(str_sub(FahrC, 2, 5), pattern="^1", "-"))/10,
DewF = 32 + 1.8 * as.integer(str_replace(str_sub(FahrC, 6, 9), pattern="^1", "-"))/10
)
# Investigate the data
cat("\n *** Parsed data structure, head, tail, and random sample *** \n")
str(dfParse)
print(head(dfParse))
print(tail(dfParse))
set.seed(seed)
dfParse %>%
sample_n(20) %>%
print()
# Check for NA values
cat("\n *** Number of NA values *** \n")
print(colSums(is.na(dfParse)))
# Plot of counts by key metric
plotcountsByMetric(dfParse, mets=metrics)
# Return the parsed dataset
dfParse
}
keyMetric <- c("WindDir", "WindSpeed", "WindGust", "Visibility", "TempC",
"DewC", "Altimeter", "SLP", "TempF", "DewF"
)
convMETAR <- convertMETAR(initMETAR, metrics=keyMetric)## Warning: NAs introduced by coercion##
## *** Parsed data structure, head, tail, and random sample ***
## Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 15 variables:
## $ METAR : chr "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ WindDir : chr "300" "000" "000" "280" ...
## $ WindSpeed : int 5 0 0 3 5 9 0 3 0 0 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 10 10 10 10 10 10 10 10 10 10 ...
## $ TempC : int -3 -3 -3 -3 -3 -6 -7 -8 -10 -9 ...
## $ DewC : int -7 -7 -7 -6 -7 -10 -11 -11 -12 -12 ...
## $ Altimeter : int 3029 3030 3030 3031 3033 3033 3034 3034 3034 3035 ...
## $ SLP : int 275 277 277 281 286 289 290 291 295 295 ...
## $ FahrC : chr "T10281067" "T10331067" "T10281067" "T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## $ origMETAR : chr "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ TempF : num 27 26.1 27 27 27 ...
## $ DewF : num 19.9 19.9 19.9 21 19.9 ...
## # A tibble: 6 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 54Z ~ 300 5 NA " " 10 -3 -7 3029 275
## 2 54Z ~ 000 0 NA " " 10 -3 -7 3030 277
## 3 54Z ~ 000 0 NA " " 10 -3 -7 3030 277
## 4 54Z ~ 280 3 NA " " 10 -3 -6 3031 281
## 5 54Z ~ 310 5 NA " " 10 -3 -7 3033 286
## 6 54Z ~ 010 9 NA " " 10 -6 -10 3033 289
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
## # A tibble: 6 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 54Z ~ 160 6 NA " " 10 2 -4 2993 147
## 2 54Z ~ VRB 4 NA " " 10 4 -4 2990 139
## 3 54Z ~ 130 7 NA " " 10 4 -7 2989 134
## 4 54Z ~ 110 7 NA " " 10 4 -7 2988 130
## 5 54Z ~ 100 10 NA " " 10 3 -5 2986 125
## 6 54Z ~ 100 10 NA " " 10 3 -6 2986 123
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
## # A tibble: 20 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 54Z ~ 360 7 NA " " 10 21 15 2995 130
## 2 54Z ~ 210 13 NA " " 10 31 4 2988 108
## 3 54Z ~ 340 11 NA " " 10 -7 -13 3025 259
## 4 54Z ~ 240 9 NA " " 10 13 8 2961 18
## 5 54Z ~ 310 6 NA " " 10 -7 -19 3044 329
## 6 54Z ~ 040 7 NA " " 10 26 14 3010 179
## 7 54Z ~ 170 17 NA " " 10 31 22 2983 90
## 8 54Z ~ 160 7 NA " " 10 -1 -4 3002 182
## 9 54Z ~ 310 9 NA " " 10 13 9 3021 226
## 10 54Z ~ 110 10 NA " " 10 16 16 2990 116
## 11 54Z ~ 140 5 NA " " 10 19 18 3013 199
## 12 54Z ~ 110 6 NA " " 10 20 4 3012 197
## 13 54Z ~ 220 4 NA " " 10 3 -4 3042 311
## 14 54Z ~ 350 3 NA " " 8 9 8 3011 194
## 15 54Z ~ 080 8 NA " " 10 -12 -18 3046 340
## 16 54Z ~ 320 7 NA " " 10 16 11 2998 144
## 17 54Z ~ 330 6 NA " " 10 9 5 3016 214
## 18 54Z ~ 340 13 NA " " 3 0 -1 2979 99
## 19 54Z ~ 190 5 NA " " 10 3 -4 3024 251
## 20 54Z ~ 320 18 NA " " 10 24 12 2999 147
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
##
## *** Number of NA values ***
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC
## 23 23 23 8813 23 23 23
## DewC Altimeter SLP FahrC dtime origMETAR TempF
## 23 23 23 23 0 0 23
## DewF
## 23
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(diff(sort(x))): no non-missing arguments to min; returning Inf## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
# There are three obvious issues
# Visibility is not correctly picked up when there is a / such as 1/2 SM
# Wind gusts are never picked up
# Sea Level Pressure is missing a digit
# Address the visibility issues
getVisibility <- function(curMet, origMet) {
# Get the original METAR data
metAll <- origMet %>%
pull(metar)
# Correct for visibility
# Areas that have \\d \\d/\\dSM
sm1 <- which(str_detect(metAll, pattern=" \\d/\\dSM"))
sm2 <- which(str_detect(metAll, pattern=" \\d \\d/\\dSM"))
valSM1 <- str_match(metAll, pattern="\\d/\\dSM")[sm1]
valSM1 <- str_replace(valSM1, "SM", "")
valSM1 <- as.integer(str_sub(valSM1, 1, 1)) / as.integer(str_sub(valSM1, 3, 3))
valSM2 <- str_match(metAll, pattern=" \\d \\d/\\dSM")[sm2]
valSM2 <- as.integer(str_sub(valSM2, 2, 2))
curMet[sm1, "Visibility"] <- valSM1
curMet[sm2, "Visibility"] <- curMet[sm2, "Visibility"] + valSM2
curMet %>%
count(Visibility) %>%
print()
curMet
}
parseMETAR <- getVisibility(convMETAR, origMet=funcMETAR)## # A tibble: 18 x 2
## Visibility n
## <dbl> <int>
## 1 0.25 20
## 2 0.5 16
## 3 0.75 15
## 4 1 19
## 5 1.25 13
## 6 1.5 17
## 7 1.75 21
## 8 2 50
## 9 2.5 38
## 10 3 70
## 11 4 108
## 12 5 108
## 13 6 146
## 14 7 189
## 15 8 221
## 16 9 290
## 17 10 7449
## 18 NA 23# Correct for wind gusts
getWindGusts <- function(curMet, origMet) {
metAll <- origMet %>%
pull(metar)
gustCheck <- which(str_detect(metAll, pattern="\\d{5}G\\d{2}KT"))
valGust <- str_match(metAll, pattern="\\d{5}G\\d{2}KT")[gustCheck]
valGust <- as.integer(str_sub(valGust, 7, 8))
curMet[gustCheck, "WindGust"] <- valGust
curMet %>%
count(WindGust) %>%
as.data.frame %>%
print()
curMet
}
parseMETAR <- getWindGusts(parseMETAR, origMet=funcMETAR)## WindGust n
## 1 14 4
## 2 15 11
## 3 16 11
## 4 17 16
## 5 18 29
## 6 19 59
## 7 20 69
## 8 21 87
## 9 22 83
## 10 23 107
## 11 24 101
## 12 25 83
## 13 26 62
## 14 27 61
## 15 28 61
## 16 29 37
## 17 30 28
## 18 31 24
## 19 32 18
## 20 33 13
## 21 34 12
## 22 35 14
## 23 36 6
## 24 37 6
## 25 38 10
## 26 39 11
## 27 40 2
## 28 41 3
## 29 42 1
## 30 43 2
## 31 45 2
## 32 NA 7780# Correct for SLP
fixSLP <- function(curMet) {
dfParse <- curMet %>%
mutate(modSLP=ifelse(curMet$SLP < 500, 1000 + curMet$SLP/10, 900 + curMet$SLP/10))
p <- dfParse %>%
group_by(SLP, modSLP) %>%
summarize(n=n()) %>%
ggplot(aes(x=SLP, y=modSLP, size=n)) +
geom_point(alpha=0.3)
print(p)
dfParse
}
parseMETAR <- fixSLP(parseMETAR)## Warning: Removed 1 rows containing missing values (geom_point).
# Check updated plots
plotcountsByMetric(parseMETAR, mets=c("WindGust", "Visibility", "modSLP"))## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
Example #19: Functional Form For Relationships Between METAR Variables
The relationships between METAR variables can also be converted to functional form.
Example code includes:
# Function to calculate, display, and plot variable correlations
corMETAR <- function(met, numVars, subT="") {
# Keep only complete cases and report on data kept
dfUse <- met %>%
select_at(vars(all_of(numVars))) %>%
filter(complete.cases(.))
nU <- nrow(dfUse)
nM <- nrow(met)
myPct <- round(100*nU/nM, 1)
cat("\n *** Correlations use ", nU, " complete cases (", myPct, "% of ", nM, " total) ***\n", sep="")
# Create the correlation matrix
mtxCorr <- dfUse %>%
cor()
# Print the correlations
mtxCorr %>%
round(2) %>%
print()
# Display a heat map
corrplot::corrplot(mtxCorr, method="color", title=paste0("Hourly Weather Correlations\n", subT))
}
# Define key numeric variables
coreNum <- c("TempC", "TempF", "DewC", "DewF", "Altimeter", "modSLP", "WindSpeed", "Visibility")
# Run the correlations function
corMETAR(parseMETAR, numVars=coreNum)##
## *** Correlations use 8790 complete cases (99.7% of 8813 total) ***
## TempC TempF DewC DewF Altimeter modSLP WindSpeed Visibility
## TempC 1.00 1.00 0.91 0.91 -0.38 -0.48 0.13 0.19
## TempF 1.00 1.00 0.91 0.91 -0.38 -0.48 0.13 0.19
## DewC 0.91 0.91 1.00 1.00 -0.38 -0.48 -0.01 0.07
## DewF 0.91 0.91 1.00 1.00 -0.38 -0.48 -0.01 0.07
## Altimeter -0.38 -0.38 -0.38 -0.38 1.00 0.99 -0.26 0.07
## modSLP -0.48 -0.48 -0.48 -0.48 0.99 1.00 -0.25 0.04
## WindSpeed 0.13 0.13 -0.01 -0.01 -0.26 -0.25 1.00 -0.01
## Visibility 0.19 0.19 0.07 0.07 0.07 0.04 -0.01 1.00
# Create a function for plotting two variables against each other
plotNumCor <- function(met, var1, var2, title=NULL, subT="") {
if (is.null(title))
{ title <- paste0("Hourly Correlations of ", var1, " and ", var2) }
p <- met %>%
group_by_at(vars(all_of(c(var1, var2)))) %>%
summarize(n=n()) %>%
ggplot(aes_string(x=var1, y=var2)) +
geom_point(alpha=0.5, aes_string(size="n")) +
geom_smooth(method="lm", aes_string(weight="n")) +
labs(x=var1, y=var2, title=title, subtitle=subT)
print(p)
}
var1List <- c("TempC", "DewC", "Altimeter", "TempF", "TempF", "TempF", "Altimeter")
var2List <- c("TempF", "DewF", "modSLP", "DewF", "Altimeter", "modSLP", "WindSpeed")
for (n in 1:length(var1List)) {
plotNumCor(parseMETAR, var1List[n], var2List[n])
}## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
# Function for linear regressions on METAR data
lmMETAR <- function(met, y, x, yName, subT="Lincoln, NE (2016) Hourly METAR") {
# Convert to formula
myChar <- paste0(y, " ~ ", x)
cat("\n *** Regression call is:", myChar, "***\n")
# Run regression
regr <- lm(formula(myChar), data=met)
# Summarize regression
print(summary(regr))
# Predict the new values
pred <- predict(regr, newdata=met)
# Plot the predictions
p <- met %>%
select_at(vars(all_of(y))) %>%
mutate(pred=pred) %>%
group_by_at(vars(all_of(c(y, "pred")))) %>%
summarize(n=n()) %>%
ggplot(aes_string(x=y, y="pred")) +
geom_point(aes(size=n), alpha=0.25) +
geom_smooth(aes(weight=n), method="lm") +
labs(title=paste0("Predicted vs. Actual ", yName, " - ", x, " as Predictor"),
subtitle=subT, x=paste0("Actual ", yName), y=paste0("Predicted ", yName)
)
print(p)
}
lmMETAR(parseMETAR, "modSLP", "Altimeter", yName="Sea Level Pressure")##
## *** Regression call is: modSLP ~ Altimeter ***
##
## Call:
## lm(formula = formula(myChar), data = met)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8890 -0.8172 -0.1578 0.7610 2.7890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.233e+01 1.406e+00 -44.34 <2e-16 ***
## Altimeter 3.594e-01 4.683e-04 767.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9691 on 8788 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.9853, Adjusted R-squared: 0.9853
## F-statistic: 5.889e+05 on 1 and 8788 DF, p-value: < 2.2e-16## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
lmMETAR(parseMETAR, "modSLP", "Altimeter + TempF", yName="Sea Level Pressure")##
## *** Regression call is: modSLP ~ Altimeter + TempF ***
##
## Call:
## lm(formula = formula(myChar), data = met)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.24130 -0.26737 0.00686 0.25214 1.23326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.002e+01 5.615e-01 -17.84 <2e-16 ***
## Altimeter 3.428e-01 1.857e-04 1845.63 <2e-16 ***
## TempF -4.559e-02 1.921e-04 -237.31 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3561 on 8787 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.998, Adjusted R-squared: 0.998
## F-statistic: 2.209e+06 on 2 and 8787 DF, p-value: < 2.2e-16## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
Example #20: Functional Form for Extracting Cloud Data from METAR
Cloud data can also be extracted using the functional form.
Example code includes:
extractClouds <- function(met, metVar, subT="Lincoln, NE (2016) Hourly METAR") {
metAll <- met %>%
pull(metVar)
# Extract the CLR records
mtxCLR <- str_extract_all(metAll, pattern=" CLR ", simplify=TRUE)
if (dim(mtxCLR)[[2]] != 1) { stop("Extracted 2+ CLR from some METAR; investigate") }
isCLR <- ifelse(mtxCLR[, 1] == "", 0, 1)
# Extract the VV records
mtxVV <- str_extract_all(metAll, pattern="VV(\\d{3})", simplify=TRUE)
if (dim(mtxVV)[[2]] > 1) { stop("Extracted 2+ VV from some METAR; investigate") }
if ((dim(mtxVV))[[2]] == 0) {
cat("\nNo Records with a cloud type of vertical visibility (VV)\n")
isVV <- rep(0, times=length(isCLR))
htVV <- rep(NA, times=length(isCLR))
} else {
isVV <- ifelse(mtxVV[, 1] == "", 0, 1)
htVV <- ifelse(mtxVV[, 1] == "", NA, as.integer(str_replace(mtxVV[, 1], "VV", ""))*100)
}
# Extract the FEW records
mtxFEW <- str_extract_all(metAll, pattern="FEW(\\d{3})", simplify=TRUE)
numFEW <- apply(mtxFEW, 1, FUN=function(x) { sum((x!=""))} )
# Extract the SCT records
mtxSCT <- str_extract_all(metAll, pattern="SCT(\\d{3})", simplify=TRUE)
numSCT <- apply(mtxSCT, 1, FUN=function(x) { sum((x!=""))} )
# Extract the BKN records
mtxBKN <- str_extract_all(metAll, pattern="BKN(\\d{3})", simplify=TRUE)
numBKN <- apply(mtxBKN, 1, FUN=function(x) { sum((x!=""))} )
# Extract the OVC records
mtxOVC <- str_extract_all(metAll, pattern="OVC(\\d{3})", simplify=TRUE)
numOVC <- apply(mtxOVC, 1, FUN=function(x) { sum((x!=""))} )
# Summarize as a data frame
tblClouds <- tibble::tibble(isCLR=isCLR, isVV=isVV, htVV=htVV, numFEW=numFEW,
numSCT=numSCT, numBKN=numBKN, numOVC=numOVC
)
# Get the counts
cat("\n*** Counts by number of layers of each cloud type ***\n")
tblClouds %>%
count(isCLR, isVV, numFEW, numSCT, numBKN, numOVC) %>%
as.data.frame() %>%
print()
# Investigate the problem data
cat("\n*** METAR records where no clouds were extracted ***\n")
metAll[rowSums(tblClouds, na.rm=TRUE)==0] %>%
print()
# Plot the counts of most obscuration
p <- tblClouds %>%
filter(rowSums(., na.rm=TRUE) > 0) %>%
mutate(wType=factor(case_when(isCLR==1 ~ "CLR", isVV==1 ~ "VV", numOVC > 0 ~ "OVC",
numBKN > 0 ~ "BKN", numSCT > 0 ~ "SCT", numFEW > 0 ~ "FEW",
TRUE ~ "Error"
), levels=c("VV", "OVC", "BKN", "SCT", "FEW", "CLR", "Error")
)
) %>%
ggplot(aes(x=wType, y=..count../sum(..count..))) +
geom_bar() +
labs(title="Highest Obscuration by Cloud", subtitle=subT,
x="Cloud Type", y="Proportion of Hourly Measurements"
)
print(p)
# Integrate the clouds data
mtxCloud <- cbind(mtxVV, mtxOVC, mtxBKN, mtxSCT, mtxFEW, mtxCLR)
cat("\n*** Dimensions for the cloud matrix ***\n")
print(dim(mtxCloud))
list(tblClouds=tblClouds, mtxCloud=mtxCloud)
}
# Run the initial cloud extraction
initClouds <- extractClouds(parseMETAR, metVar="origMETAR")##
## *** Counts by number of layers of each cloud type ***
## isCLR isVV numFEW numSCT numBKN numOVC n
## 1 0 0 0 0 0 0 6
## 2 0 0 0 0 0 1 1389
## 3 0 0 0 0 1 0 307
## 4 0 0 0 0 1 1 250
## 5 0 0 0 0 2 0 50
## 6 0 0 0 0 2 1 45
## 7 0 0 0 0 3 0 8
## 8 0 0 0 1 0 0 230
## 9 0 0 0 1 0 1 73
## 10 0 0 0 1 1 0 52
## 11 0 0 0 1 1 1 42
## 12 0 0 0 1 2 0 17
## 13 0 0 0 2 0 0 16
## 14 0 0 0 2 0 1 9
## 15 0 0 0 2 1 0 6
## 16 0 0 1 0 0 0 380
## 17 0 0 1 0 0 1 90
## 18 0 0 1 0 1 0 46
## 19 0 0 1 0 1 1 62
## 20 0 0 1 0 2 0 9
## 21 0 0 1 1 0 0 39
## 22 0 0 1 1 0 1 24
## 23 0 0 1 1 1 0 28
## 24 0 0 1 2 0 0 10
## 25 0 0 2 0 0 0 24
## 26 0 0 2 0 0 1 6
## 27 0 0 2 0 1 0 5
## 28 0 0 2 1 0 0 3
## 29 0 0 3 0 0 0 2
## 30 0 1 0 0 0 0 33
## 31 1 0 0 0 0 0 5552
##
## *** METAR records where no clouds were extracted ***
## [1] "KLNK 301854Z 17011KT 10SM 30/18 A2993 RMK AO2 SLP124 T03000178 $"
## [2] "KLNK 011454Z 21/10 A3008 RMK AO2 SLP178 T02110100 51011 $"
## [3] "KLNK 261554Z 05006KT 27/18 A3013 RMK AO2 SLP190 T02670178 RVRNO $"
## [4] "KLNK 161554Z 09009KT 10SM 26/19 A3007 RMK AO2 SLP171 T02610194 $"
## [5] "KLNK 211954Z 25008KT 210V270 10SM 27/10 A3005 RMK AO2 SLP170 T02720100 $"
## [6] "KLNK 050854Z 00000KT 1/2SM R36/3000VP6000FT FG 09/09 A2978 RMK AO2 SLP081 T00940094 51008 $"
##
## *** Dimensions for the cloud matrix ***
## [1] 8813 11str(initClouds)## List of 2
## $ tblClouds:Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 7 variables:
## ..$ isCLR : num [1:8813] 0 0 0 0 0 0 0 0 0 1 ...
## ..$ isVV : num [1:8813] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ htVV : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ numFEW: int [1:8813] 0 0 0 0 0 0 0 0 1 0 ...
## ..$ numSCT: int [1:8813] 0 0 0 0 1 0 0 0 0 0 ...
## ..$ numBKN: int [1:8813] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numOVC: int [1:8813] 1 1 1 1 1 1 1 1 0 0 ...
## $ mtxCloud : chr [1:8813, 1:11] "" "" "" "" ...# Cycle through to find levels of a given type
ckClouds <- function(cloudType, mtx) {
isKey <- which(apply(mtx, 2, FUN=function(x) {sum(str_detect(x, cloudType))}) > 0)
as.integer(str_replace(mtx[, min(isKey)], cloudType, "")) * 100
}
# Function to create the lowest cloud levels
findLowestClouds <- function(mtxCloud, subT="Lincoln, NE (2016) Hourly METAR") {
# Find the lowest clouds by cloud type
lowOVC <- ckClouds("OVC", mtx=mtxCloud)
lowVV <- ckClouds("VV", mtx=mtxCloud)
lowBKN <- ckClouds("BKN", mtx=mtxCloud)
lowSCT <- ckClouds("SCT", mtx=mtxCloud)
lowFEW <- ckClouds("FEW", mtx=mtxCloud)
# Integrate the lowest cloud type by level
lowCloud <- tibble::tibble(lowVV, lowOVC, lowBKN, lowSCT, lowFEW)
cat("\n*** Lowest clouds by type tibble ***\n")
print(lowCloud)
# Get the lowest cloud level
minCloud <- lowCloud
minCloud[is.na(minCloud)] <- 999999
minCloudLevel <- apply(minCloud, 1, FUN=min)
minCeilingLevel <- apply(minCloud[, c("lowVV", "lowOVC", "lowBKN")], 1, FUN=min)
noCloudPct <- mean(minCloudLevel == 999999)
noCeilingPct <- mean(minCeilingLevel == 999999)
# Plot the minimum cloud level (where it exists)
p <- data.frame(minCloudLevel, minCeilingLevel) %>%
filter(minCloudLevel != 999999) %>%
ggplot(aes(x=minCloudLevel)) +
geom_bar(aes(y=..count../sum(..count..))) +
geom_text(aes(x=2500, y=0.04,
label=paste0(round(100*noCloudPct), "% of obs. have no clouds")
)
) +
labs(x="Height [ft]", y="Proportion",
title="Minimum Cloud Height (when some clouds exist)", subtitle=subT
)
print(p)
# Plot the minimum ceiling level (where it exists)
p <- data.frame(minCloudLevel, minCeilingLevel) %>%
filter(minCeilingLevel != 999999) %>%
ggplot(aes(x=minCeilingLevel)) +
geom_bar(aes(y=..count../sum(..count..))) +
geom_text(aes(x=2500, y=0.04,
label=paste0(round(100*noCeilingPct), "% of obs. have no ceiling")
)
) +
labs(x="Height [ft]", y="Proportion",
title="Minimum Ceiling Height (when a ceiling exists)", subtitle=subT
)
print(p)
list(lowCloud=lowCloud, minCeilingLevel=minCeilingLevel, minCloudLevel=minCloudLevel)
}
processedClouds <-findLowestClouds(initClouds$mtxCloud)##
## *** Lowest clouds by type tibble ***
## # A tibble: 8,813 x 5
## lowVV lowOVC lowBKN lowSCT lowFEW
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NA 2800 NA NA NA
## 2 NA 2700 NA NA NA
## 3 NA 2600 NA NA NA
## 4 NA 2700 NA NA NA
## 5 NA 2700 NA 2100 NA
## 6 NA 2700 NA NA NA
## 7 NA 2700 NA NA NA
## 8 NA 2700 NA NA NA
## 9 NA NA NA NA 2600
## 10 NA NA NA NA NA
## # ... with 8,803 more rows
str(processedClouds)## List of 3
## $ lowCloud :Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 5 variables:
## ..$ lowVV : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowOVC: num [1:8813] 2800 2700 2600 2700 2700 2700 2700 2700 NA NA ...
## ..$ lowBKN: num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowSCT: num [1:8813] NA NA NA NA 2100 NA NA NA NA NA ...
## ..$ lowFEW: num [1:8813] NA NA NA NA NA NA NA NA 2600 NA ...
## $ minCeilingLevel: num [1:8813] 2800 2700 2600 2700 2700 ...
## $ minCloudLevel : num [1:8813] 2800 2700 2600 2700 2100 ...Example #21: Functional Form for Plotting by factor variables
The month of the year is an interesting data point for plotting against.
Example code includes:
# Function to bind the existing parsed METAR data with the cloud data
bindMETAR <- function(dfParse, tblClouds, lowCloud) {
# Integrate the cloud data and convert month to a factor
dfFull <- cbind(dfParse, tblClouds, lowCloud) %>%
mutate(wType=factor(case_when(isCLR==1 ~ "CLR", isVV==1 ~ "VV", numOVC > 0 ~ "OVC",
numBKN > 0 ~ "BKN", numSCT > 0 ~ "SCT", numFEW > 0 ~ "FEW",
TRUE ~ "Error"
), levels=c("VV", "OVC", "BKN", "SCT", "FEW", "CLR", "Error")
),
month=factor(lubridate::month(dtime), levels=1:12, labels=month.abb)
)
dfFull <- tibble::as_tibble(dfFull)
str(dfFull)
dfFull
}
fullMETAR <- bindMETAR(dfParse=parseMETAR,
tblClouds=initClouds$tblClouds,
lowCloud=processedClouds$lowCloud
)## Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 30 variables:
## $ METAR : chr "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ WindDir : chr "300" "000" "000" "280" ...
## $ WindSpeed : int 5 0 0 3 5 9 0 3 0 0 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 10 10 10 10 10 10 10 10 10 10 ...
## $ TempC : int -3 -3 -3 -3 -3 -6 -7 -8 -10 -9 ...
## $ DewC : int -7 -7 -7 -6 -7 -10 -11 -11 -12 -12 ...
## $ Altimeter : int 3029 3030 3030 3031 3033 3033 3034 3034 3034 3035 ...
## $ SLP : int 275 277 277 281 286 289 290 291 295 295 ...
## $ FahrC : chr "T10281067" "T10331067" "T10281067" "T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## $ origMETAR : chr "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ TempF : num 27 26.1 27 27 27 ...
## $ DewF : num 19.9 19.9 19.9 21 19.9 ...
## $ modSLP : num 1028 1028 1028 1028 1029 ...
## $ isCLR : num 0 0 0 0 0 0 0 0 0 1 ...
## $ isVV : num 0 0 0 0 0 0 0 0 0 0 ...
## $ htVV : num NA NA NA NA NA NA NA NA NA NA ...
## $ numFEW : int 0 0 0 0 0 0 0 0 1 0 ...
## $ numSCT : int 0 0 0 0 1 0 0 0 0 0 ...
## $ numBKN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ numOVC : int 1 1 1 1 1 1 1 1 0 0 ...
## $ lowVV : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowOVC : num 2800 2700 2600 2700 2700 2700 2700 2700 NA NA ...
## $ lowBKN : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowSCT : num NA NA NA NA 2100 NA NA NA NA NA ...
## $ lowFEW : num NA NA NA NA NA NA NA NA 2600 NA ...
## $ wType : Factor w/ 7 levels "VV","OVC","BKN",..: 2 2 2 2 2 2 2 2 5 6 ...
## $ month : Factor w/ 12 levels "Jan","Feb","Mar",..: 12 12 12 12 12 12 12 12 12 12 ...# Updated function for plotting numeric by factor
plotFactorNumeric <- function(met, fctVar, numVar, title=NULL, subT) {
if (is.null(title)) { title <- paste0("Hourly Weather - ", numVar, " vs. ", fctVar) }
p <- met %>%
filter(!is.na(get(fctVar)), !is.na(get(numVar))) %>%
ggplot(aes_string(x=fctVar, y=numVar)) +
geom_boxplot(fill="lightblue") +
labs(title=title, subtitle=subT)
print(p)
}
# Function for creating cloud plots
makeFactorPlots <- function(met,
fctVar=c("month", "wType"),
keyVar=c("WindSpeed", "Visibility", "Altimeter", "TempF", "DewF"),
desc="Lincoln, NE (2016) Hourly METAR"
) {
# Run for all of the key variables against wind speed and cloud type
for (varF in fctVar) {
for (varK in keyVar) {
plotFactorNumeric(met, fctVar=varF, numVar=varK, subT=desc)
}
}
# Create stacked bars for cloud type by month
# dfFull %>%
# filter(!is.na(wType), wType!="Error") %>%
# ggplot(aes(x=month, fill=wType)) +
# geom_bar(position="fill") +
# labs(title="Lincoln, NE (2016)", x="", y="Proportion of Month")
}
makeFactorPlots(fullMETAR)
Example #22: Combining Functional Forms for METAR Processing
The functions can be combined in to a single routine for reading, parsing, and running EDA on METAR data..
Example code includes:
# Function to run the full process
runAllMETAR <- function(fname, timeZ, expMin, expDays, locMET, shortMET, longMET, valMet,
labsMet=c("METAR", "WindDir", "WindSpeed", "WindGust", "Dummy", "Visibility",
"TempC", "DewC", "Altimeter", "SLP", "FahrC"
),
keyMetric=c("WindDir", "WindSpeed", "WindGust", "Visibility", "TempC",
"DewC", "Altimeter", "SLP", "TempF", "DewF"
),
coreNum=c("TempC", "TempF", "DewC", "DewF",
"Altimeter", "modSLP", "WindSpeed", "Visibility"
),
var1List=c("TempC", "DewC", "Altimeter", "TempF", "TempF", "TempF", "Altimeter"),
var2List=c("TempF", "DewF", "modSLP", "DewF", "Altimeter", "modSLP", "WindSpeed")
) {
# Read in the METAR data
funcMETAR <- readMETAR(fileName=fname, timeZ=timeZ, expMin=expMin, expDays=expDays)
# funcMETAR
# Extract wind data from METAR
windMETAR <- extractWind(funcMETAR)
# windMETAR
# Run basic wind plots
basicWindPlots(windMETAR, desc=locMET, gran=shortMET)
# Run the METAR parsing on the raw data
initMETAR <- initialParseMETAR(funcMETAR, val=valMet, labs=labsMet)
# initMETAR
# Parse and convert the METAR data
convMETAR <- convertMETAR(initMETAR, metrics=keyMetric)
# convMETAR
# Fix problems with visibility, wind gusts, and SLP
parseMETAR <- getVisibility(convMETAR, origMet=funcMETAR)
parseMETAR <- getWindGusts(parseMETAR, origMet=funcMETAR)
parseMETAR <- fixSLP(parseMETAR)
# Check updated plots
plotcountsByMetric(parseMETAR, mets=c("WindGust", "Visibility", "modSLP"))
# Run the correlations function
corMETAR(parseMETAR, numVars=coreNum, subT=longMET)
# Plot correlations
for (n in 1:length(var1List)) {
plotNumCor(parseMETAR, var1List[n], var2List[n], subT=longMET)
}
# Run lm models for SLP vs Altimeter and (optionally) Temperature
lmMETAR(parseMETAR, "modSLP", "Altimeter", yName="Sea Level Pressure", subT=longMET)
lmMETAR(parseMETAR, "modSLP", "Altimeter + TempF", yName="Sea Level Pressure", subT=longMET)
# Run the initial cloud extraction
initClouds <- extractClouds(parseMETAR, metVar="origMETAR", subT=longMET)
str(initClouds)
# Find the lowest cloud levels and lowest ceilings
processedClouds <-findLowestClouds(initClouds$mtxCloud, subT=longMET)
str(processedClouds)
# Bind the processed METAR and the cloud data
fullMETAR <- bindMETAR(dfParse=parseMETAR,
tblClouds=initClouds$tblClouds,
lowCloud=processedClouds$lowCloud
)
# Create box plots for key weather elements against month and cloud type
makeFactorPlots(fullMETAR, desc=longMET)
# Return all of the elements
list(fullMETAR=fullMETAR, funcMETAR=funcMETAR, windMETAR=windMETAR,
initMETAR=initMETAR, convMETAR=convMETAR, parseMETAR=parseMETAR,
initClouds=initClouds, processedClouds=processedClouds
)
}Followed by caching the run of the function for Lincoln, NE:
# Set key parameters for reading and interpreting METAR
fname <- "./RInputFiles/metar_klnk_2016.txt" # file name for raw METAR data
timeZ <- "54Z" # Zulu time that METAR is recorded at this station
expMin <- as.POSIXct("2015-12-31 00:54:00", tz="UTC") # Expected first time read
expDays <- 368 # Expected total days read
locMET <- "Lincoln, NE" # Description of city or location
shortMET <- "KLNK METAR (2016)" # Station code and timing
longMET <- "Lincoln, NE Hourly METAR (2016)" # Description of city or location and timing
# Extraction format for METAR - paste the expected Zulu time at the front
valMet <- paste0(timeZ, ".*?(VRB|\\d{3})(\\d{2})(G\\d{2})?KT(.*?)(\\d{1,2}SM).*?\\s(M?\\d{2})/(M?\\d{2}).*?(A\\d{4}).*?RMK.*?(SLP\\d{3}).*?(T\\d{8})")
# Run the process for Lincoln, NE
klnk2016METAR <- runAllMETAR(fname=fname, timeZ=timeZ, expMin=expMin, expDays=expDays,
locMET=locMET, shortMET=shortMET, longMET=longMET, valMet=valMet
)## Parsed with column specification:
## cols(
## .default = col_double(),
## station = col_character(),
## valid = col_datetime(format = ""),
## p01i = col_character(),
## skyc1 = col_character(),
## skyc2 = col_character(),
## skyc3 = col_character(),
## skyc4 = col_logical(),
## skyl4 = col_logical(),
## wxcodes = col_character(),
## ice_accretion_1hr = col_character(),
## ice_accretion_3hr = col_character(),
## ice_accretion_6hr = col_character(),
## peak_wind_time = col_datetime(format = ""),
## metar = col_character()
## )## See spec(...) for full column specifications.## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 10594 obs. of 29 variables:
## $ station : chr "LNK" "LNK" "LNK" "LNK" ...
## $ valid : POSIXct, format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## $ tmpf : num 27 26.1 27 27 27 ...
## $ dwpf : num 19.9 19.9 19.9 21 19.9 ...
## $ relh : num 74.5 77.3 74.5 78 74.5 ...
## $ drct : num 300 0 0 280 310 10 0 10 20 0 ...
## $ sknt : num 5 0 0 3 5 9 0 3 3 0 ...
## $ p01i : chr "0.00" "0.00" "0.00" "0.00" ...
## $ alti : num 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num 1028 1028 1028 1028 1029 ...
## $ vsby : num 10 10 10 10 10 10 10 10 10 10 ...
## $ gust : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr "OVC" "OVC" "OVC" "OVC" ...
## $ skyc2 : chr NA NA NA NA ...
## $ skyc3 : chr NA NA NA NA ...
## $ skyc4 : logi NA NA NA NA NA NA ...
## $ skyl1 : num 2800 2700 2600 2700 2100 2700 2700 2700 2600 2600 ...
## $ skyl2 : num NA NA NA NA 2700 NA NA NA NA NA ...
## $ skyl3 : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyl4 : logi NA NA NA NA NA NA ...
## $ wxcodes : chr NA NA NA NA ...
## $ ice_accretion_1hr: chr NA NA NA NA ...
## $ ice_accretion_3hr: chr NA NA NA NA ...
## $ ice_accretion_6hr: chr NA NA NA NA ...
## $ peak_wind_gust : num NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_drct : num NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_time : POSIXct, format: NA NA ...
## $ feel : num 20.4 26.1 27 22.9 20.4 ...
## $ metar : chr "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
##
## *** OBSERVATIONS EXPECTED BUT NOT RECORDED ***
## [1] "2016-01-19 11:54:00 UTC" "2016-05-06 11:54:00 UTC"
## [3] "2016-05-06 12:54:00 UTC" "2016-06-17 23:54:00 UTC"
## [5] "2016-06-18 00:54:00 UTC" "2016-06-18 07:54:00 UTC"
## [7] "2016-07-02 15:54:00 UTC" "2016-07-13 14:54:00 UTC"
## [9] "2016-07-13 15:54:00 UTC" "2016-07-13 16:54:00 UTC"
## [11] "2016-07-13 17:54:00 UTC" "2016-07-30 13:54:00 UTC"
## [13] "2016-08-02 07:54:00 UTC" "2016-08-05 07:54:00 UTC"
## [15] "2016-08-29 21:54:00 UTC" "2016-09-15 16:54:00 UTC"
## [17] "2016-09-16 05:54:00 UTC" "2016-11-21 00:54:00 UTC"
## [19] "2016-12-03 08:54:00 UTC"
##
## *** OBSERVATIONS RECORDED BUT NOT EXPECTED ***
## POSIXct of length 0
##
## *** Are the extracted records unique? ***
## [1] TRUE
##
##
## *** First 6 winds and parsing ***
## [,1] [,2] [,3] [,4]
## [1,] "30005KT" "300" "05" NA
## [2,] "00000KT" "000" "00" NA
## [3,] "00000KT" "000" "00" NA
## [4,] "28003KT" "280" "03" NA
## [5,] "31005KT" "310" "05" NA
## [6,] "01009KT" "010" "09" NA
##
## *** Table of WIND DIRECTION ***
##
## 000 010 020 030 040 050 060 070 080 090 100 110 120 130 140 150
## 875 269 199 146 135 121 108 95 88 65 102 158 169 245 241 339
## 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310
## 463 565 517 413 284 179 142 96 73 80 105 89 121 141 147 225
## 320 330 340 350 360 VRB <NA>
## 234 303 352 413 383 114 19
##
## *** Table of WIND SPEED ***
##
## 00 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17
## 875 615 704 664 696 651 636 599 536 451 439 371 315 276 235 173
## 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 34
## 156 108 69 62 45 33 23 13 14 9 10 6 6 2 1 1
## <NA>
## 19
##
## *** Table of WIND GUST ***
##
## G14 G15 G16 G17 G18 G19 G20 G21 G22 G23 G24 G25 G26 G27 G28 G29
## 6 11 11 16 30 59 69 87 83 107 101 83 62 61 61 37
## G30 G31 G32 G33 G34 G35 G36 G37 G38 G39 G40 G41 G42 G43 G45 <NA>
## 28 24 18 13 12 14 6 6 10 11 2 3 1 2 2 7777
##
## *** WIND DATA WAS NOT CAPTURED FROM: ***
## # A tibble: 19 x 1
## metar
## <chr>
## 1 KLNK 291354Z 10SM CLR M01/M03 A2976 RMK AO2 SLP088 T10061028
## 2 KLNK 012154Z 10SM CLR 01/M08 A3018 RMK AO2 SLP234 T00061083
## 3 KLNK 201754Z 10SM CLR 06/M07 A3041 RMK AO2 SLP308 T00611067 10061 21039 58013
## 4 KLNK 221754Z 10SM CLR 16/03 A2955 RMK AO2 SLP000 T01610033 10161 20106 58006~
## 5 KLNK 221854Z 10SM CLR 17/03 A2954 RMK AO2 SLP000 T01670033 $
## 6 KLNK 050254Z 10SM CLR 14/03 A3004 RMK AO2 SLP169 T01390033 53007
## 7 KLNK 181754Z 10SM OVC075 21/04 A3023 RMK AO2 SLP234 T02110039 10217 20072 58~
## 8 KLNK 011454Z 21/10 A3008 RMK AO2 SLP178 T02110100 51011 $
## 9 KLNK 152254Z 10SM CLR 38/11 A2978 RMK AO2 SLP070 T03780106
## 10 KLNK 181954Z 10SM SCT045 31/20 A3017 RMK AO2 SLP205 T03060200
## 11 KLNK 261654Z 10SM CLR 29/18 A3014 RMK AO2 SLP192 T02890183 $
## 12 KLNK 261754Z 10SM CLR 31/18 A3012 RMK AO2 SLP188 T03060178 10306 20222
## 13 KLNK 271754Z 10SM CLR 33/18 A3015 RMK AO2 SLP198 T03330183 10333 20222 55001
## 14 KLNK 251754Z 10SM CLR 29/17 A3013 RMK AO2 SLP192 T02890167 10289 20194 58010
## 15 KLNK 261854Z 10SM CLR 30/18 A3006 RMK AO2 SLP166 T03000183
## 16 KLNK 211854Z 10SM CLR 27/10 A3007 RMK AO2 SLP174 T02670100
## 17 KLNK 201454Z 10SM CLR 24/21 A3008 RMK AO2 SLP178 T02390206 56004 $
## 18 KLNK 051854Z 10SM CLR 22/07 A3027 RMK AO2 SLP244 T02170067
## 19 KLNK 170754Z AUTO 10SM CLR 08/05 A2950 RMK AO2 SLP984 T00830050
## Warning: Removed 19 rows containing non-finite values (stat_count).
## Warning: Removed 4 rows containing missing values (geom_point).##
## *** Tentative Summary of Element Parsing ***
## .
## FALSE TRUE
## 23 8790
##
## *** Data Not Matched ***
## [1] "KLNK 291354Z 10SM CLR M01/M03 A2976 RMK AO2 SLP088 T10061028"
## [2] "KLNK 012154Z 10SM CLR 01/M08 A3018 RMK AO2 SLP234 T00061083"
## [3] "KLNK 201754Z 10SM CLR 06/M07 A3041 RMK AO2 SLP308 T00611067 10061 21039 58013"
## [4] "KLNK 221654Z 19007KT CLR 15/03 A2956 RMK AO2 SLP006 T01500033 $"
## [5] "KLNK 221754Z 10SM CLR 16/03 A2955 RMK AO2 SLP000 T01610033 10161 20106 58006 $"
## [6] "KLNK 221854Z 10SM CLR 17/03 A2954 RMK AO2 SLP000 T01670033 $"
## [7] "KLNK 050254Z 10SM CLR 14/03 A3004 RMK AO2 SLP169 T01390033 53007"
## [8] "KLNK 181754Z 10SM OVC075 21/04 A3023 RMK AO2 SLP234 T02110039 10217 20072 58011"
## [9] "KLNK 011454Z 21/10 A3008 RMK AO2 SLP178 T02110100 51011 $"
## [10] "KLNK 011554Z 32007KT CLR 22/09 A3008 RMK AO2 SLP179 T02170089 $"
## [11] "KLNK 152254Z 10SM CLR 38/11 A2978 RMK AO2 SLP070 T03780106"
## [12] "KLNK 181954Z 10SM SCT045 31/20 A3017 RMK AO2 SLP205 T03060200"
## [13] "KLNK 261554Z 05006KT 27/18 A3013 RMK AO2 SLP190 T02670178 RVRNO $"
## [14] "KLNK 261654Z 10SM CLR 29/18 A3014 RMK AO2 SLP192 T02890183 $"
## [15] "KLNK 261754Z 10SM CLR 31/18 A3012 RMK AO2 SLP188 T03060178 10306 20222"
## [16] "KLNK 271754Z 10SM CLR 33/18 A3015 RMK AO2 SLP198 T03330183 10333 20222 55001"
## [17] "KLNK 251754Z 10SM CLR 29/17 A3013 RMK AO2 SLP192 T02890167 10289 20194 58010"
## [18] "KLNK 261854Z 10SM CLR 30/18 A3006 RMK AO2 SLP166 T03000183"
## [19] "KLNK 211854Z 10SM CLR 27/10 A3007 RMK AO2 SLP174 T02670100"
## [20] "KLNK 201454Z 10SM CLR 24/21 A3008 RMK AO2 SLP178 T02390206 56004 $"
## [21] "KLNK 201554Z 15014G19KT CLR 27/22 A3007 RMK AO2 SLP174 T02670217 $"
## [22] "KLNK 051854Z 10SM CLR 22/07 A3027 RMK AO2 SLP244 T02170067"
## [23] "KLNK 170754Z AUTO 10SM CLR 08/05 A2950 RMK AO2 SLP984 T00830050"
##
## *** Parsing matrix summary ***
## [1] 8813 11
## [,1]
## [1,] "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067"
## [2,] "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067"
## [3,] "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067"
## [4,] "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061"
## [5,] "54Z 31005KT 10SM SCT021 OVC027 M03/M07 A3033 RMK AO2 SLP286 T10281067"
## [6,] "54Z AUTO 01009KT 10SM OVC027 M06/M10 A3033 RMK AO2 SLP289 T10611100"
## [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
## [1,] "300" "05" NA " " "10SM" "M03" "M07" "A3029" "SLP275" "T10281067"
## [2,] "000" "00" NA " " "10SM" "M03" "M07" "A3030" "SLP277" "T10331067"
## [3,] "000" "00" NA " " "10SM" "M03" "M07" "A3030" "SLP277" "T10281067"
## [4,] "280" "03" NA " " "10SM" "M03" "M06" "A3031" "SLP281" "T10281061"
## [5,] "310" "05" NA " " "10SM" "M03" "M07" "A3033" "SLP286" "T10281067"
## [6,] "010" "09" NA " " "10SM" "M06" "M10" "A3033" "SLP289" "T10611100"
##
## *** Summary of the parsed data ***
## Observations: 8,813
## Variables: 13
## $ METAR <chr> "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T1...
## $ WindDir <chr> "300", "000", "000", "280", "310", "010", "000", "010", ...
## $ WindSpeed <chr> "05", "00", "00", "03", "05", "09", "00", "03", "00", "0...
## $ WindGust <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ Dummy <chr> " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "...
## $ Visibility <chr> "10SM", "10SM", "10SM", "10SM", "10SM", "10SM", "10SM", ...
## $ TempC <chr> "M03", "M03", "M03", "M03", "M03", "M06", "M07", "M08", ...
## $ DewC <chr> "M07", "M07", "M07", "M06", "M07", "M10", "M11", "M11", ...
## $ Altimeter <chr> "A3029", "A3030", "A3030", "A3031", "A3033", "A3033", "A...
## $ SLP <chr> "SLP275", "SLP277", "SLP277", "SLP281", "SLP286", "SLP28...
## $ FahrC <chr> "T10281067", "T10331067", "T10281067", "T10281061", "T10...
## $ dtime <dttm> 2015-12-31 00:54:00, 2015-12-31 01:54:00, 2015-12-31 02...
## $ origMETAR <chr> "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 ...## Warning: NAs introduced by coercion
##
## *** Parsed data structure, head, tail, and random sample ***
## Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 15 variables:
## $ METAR : chr "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ WindDir : chr "300" "000" "000" "280" ...
## $ WindSpeed : int 5 0 0 3 5 9 0 3 0 0 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 10 10 10 10 10 10 10 10 10 10 ...
## $ TempC : int -3 -3 -3 -3 -3 -6 -7 -8 -10 -9 ...
## $ DewC : int -7 -7 -7 -6 -7 -10 -11 -11 -12 -12 ...
## $ Altimeter : int 3029 3030 3030 3031 3033 3033 3034 3034 3034 3035 ...
## $ SLP : int 275 277 277 281 286 289 290 291 295 295 ...
## $ FahrC : chr "T10281067" "T10331067" "T10281067" "T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## $ origMETAR : chr "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ TempF : num 27 26.1 27 27 27 ...
## $ DewF : num 19.9 19.9 19.9 21 19.9 ...
## # A tibble: 6 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 54Z ~ 300 5 NA " " 10 -3 -7 3029 275
## 2 54Z ~ 000 0 NA " " 10 -3 -7 3030 277
## 3 54Z ~ 000 0 NA " " 10 -3 -7 3030 277
## 4 54Z ~ 280 3 NA " " 10 -3 -6 3031 281
## 5 54Z ~ 310 5 NA " " 10 -3 -7 3033 286
## 6 54Z ~ 010 9 NA " " 10 -6 -10 3033 289
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
## # A tibble: 6 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 54Z ~ 160 6 NA " " 10 2 -4 2993 147
## 2 54Z ~ VRB 4 NA " " 10 4 -4 2990 139
## 3 54Z ~ 130 7 NA " " 10 4 -7 2989 134
## 4 54Z ~ 110 7 NA " " 10 4 -7 2988 130
## 5 54Z ~ 100 10 NA " " 10 3 -5 2986 125
## 6 54Z ~ 100 10 NA " " 10 3 -6 2986 123
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
## # A tibble: 20 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 54Z ~ 360 7 NA " " 10 21 15 2995 130
## 2 54Z ~ 210 13 NA " " 10 31 4 2988 108
## 3 54Z ~ 340 11 NA " " 10 -7 -13 3025 259
## 4 54Z ~ 240 9 NA " " 10 13 8 2961 18
## 5 54Z ~ 310 6 NA " " 10 -7 -19 3044 329
## 6 54Z ~ 040 7 NA " " 10 26 14 3010 179
## 7 54Z ~ 170 17 NA " " 10 31 22 2983 90
## 8 54Z ~ 160 7 NA " " 10 -1 -4 3002 182
## 9 54Z ~ 310 9 NA " " 10 13 9 3021 226
## 10 54Z ~ 110 10 NA " " 10 16 16 2990 116
## 11 54Z ~ 140 5 NA " " 10 19 18 3013 199
## 12 54Z ~ 110 6 NA " " 10 20 4 3012 197
## 13 54Z ~ 220 4 NA " " 10 3 -4 3042 311
## 14 54Z ~ 350 3 NA " " 8 9 8 3011 194
## 15 54Z ~ 080 8 NA " " 10 -12 -18 3046 340
## 16 54Z ~ 320 7 NA " " 10 16 11 2998 144
## 17 54Z ~ 330 6 NA " " 10 9 5 3016 214
## 18 54Z ~ 340 13 NA " " 3 0 -1 2979 99
## 19 54Z ~ 190 5 NA " " 10 3 -4 3024 251
## 20 54Z ~ 320 18 NA " " 10 24 12 2999 147
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
##
## *** Number of NA values ***
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC
## 23 23 23 8813 23 23 23
## DewC Altimeter SLP FahrC dtime origMETAR TempF
## 23 23 23 23 0 0 23
## DewF
## 23
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(diff(sort(x))): no non-missing arguments to min; returning Inf## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## # A tibble: 18 x 2
## Visibility n
## <dbl> <int>
## 1 0.25 20
## 2 0.5 16
## 3 0.75 15
## 4 1 19
## 5 1.25 13
## 6 1.5 17
## 7 1.75 21
## 8 2 50
## 9 2.5 38
## 10 3 70
## 11 4 108
## 12 5 108
## 13 6 146
## 14 7 189
## 15 8 221
## 16 9 290
## 17 10 7449
## 18 NA 23
## WindGust n
## 1 14 4
## 2 15 11
## 3 16 11
## 4 17 16
## 5 18 29
## 6 19 59
## 7 20 69
## 8 21 87
## 9 22 83
## 10 23 107
## 11 24 101
## 12 25 83
## 13 26 62
## 14 27 61
## 15 28 61
## 16 29 37
## 17 30 28
## 18 31 24
## 19 32 18
## 20 33 13
## 21 34 12
## 22 35 14
## 23 36 6
## 24 37 6
## 25 38 10
## 26 39 11
## 27 40 2
## 28 41 3
## 29 42 1
## 30 43 2
## 31 45 2
## 32 NA 7780## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
##
## *** Correlations use 8790 complete cases (99.7% of 8813 total) ***
## TempC TempF DewC DewF Altimeter modSLP WindSpeed Visibility
## TempC 1.00 1.00 0.91 0.91 -0.38 -0.48 0.13 0.19
## TempF 1.00 1.00 0.91 0.91 -0.38 -0.48 0.13 0.19
## DewC 0.91 0.91 1.00 1.00 -0.38 -0.48 -0.01 0.07
## DewF 0.91 0.91 1.00 1.00 -0.38 -0.48 -0.01 0.07
## Altimeter -0.38 -0.38 -0.38 -0.38 1.00 0.99 -0.26 0.07
## modSLP -0.48 -0.48 -0.48 -0.48 0.99 1.00 -0.25 0.04
## WindSpeed 0.13 0.13 -0.01 -0.01 -0.26 -0.25 1.00 -0.01
## Visibility 0.19 0.19 0.07 0.07 0.07 0.04 -0.01 1.00
## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
##
## *** Regression call is: modSLP ~ Altimeter ***
##
## Call:
## lm(formula = formula(myChar), data = met)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8890 -0.8172 -0.1578 0.7610 2.7890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.233e+01 1.406e+00 -44.34 <2e-16 ***
## Altimeter 3.594e-01 4.683e-04 767.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9691 on 8788 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.9853, Adjusted R-squared: 0.9853
## F-statistic: 5.889e+05 on 1 and 8788 DF, p-value: < 2.2e-16## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
##
## *** Regression call is: modSLP ~ Altimeter + TempF ***
##
## Call:
## lm(formula = formula(myChar), data = met)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.24130 -0.26737 0.00686 0.25214 1.23326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.002e+01 5.615e-01 -17.84 <2e-16 ***
## Altimeter 3.428e-01 1.857e-04 1845.63 <2e-16 ***
## TempF -4.559e-02 1.921e-04 -237.31 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3561 on 8787 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.998, Adjusted R-squared: 0.998
## F-statistic: 2.209e+06 on 2 and 8787 DF, p-value: < 2.2e-16## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
##
## *** Counts by number of layers of each cloud type ***
## isCLR isVV numFEW numSCT numBKN numOVC n
## 1 0 0 0 0 0 0 6
## 2 0 0 0 0 0 1 1389
## 3 0 0 0 0 1 0 307
## 4 0 0 0 0 1 1 250
## 5 0 0 0 0 2 0 50
## 6 0 0 0 0 2 1 45
## 7 0 0 0 0 3 0 8
## 8 0 0 0 1 0 0 230
## 9 0 0 0 1 0 1 73
## 10 0 0 0 1 1 0 52
## 11 0 0 0 1 1 1 42
## 12 0 0 0 1 2 0 17
## 13 0 0 0 2 0 0 16
## 14 0 0 0 2 0 1 9
## 15 0 0 0 2 1 0 6
## 16 0 0 1 0 0 0 380
## 17 0 0 1 0 0 1 90
## 18 0 0 1 0 1 0 46
## 19 0 0 1 0 1 1 62
## 20 0 0 1 0 2 0 9
## 21 0 0 1 1 0 0 39
## 22 0 0 1 1 0 1 24
## 23 0 0 1 1 1 0 28
## 24 0 0 1 2 0 0 10
## 25 0 0 2 0 0 0 24
## 26 0 0 2 0 0 1 6
## 27 0 0 2 0 1 0 5
## 28 0 0 2 1 0 0 3
## 29 0 0 3 0 0 0 2
## 30 0 1 0 0 0 0 33
## 31 1 0 0 0 0 0 5552
##
## *** METAR records where no clouds were extracted ***
## [1] "KLNK 301854Z 17011KT 10SM 30/18 A2993 RMK AO2 SLP124 T03000178 $"
## [2] "KLNK 011454Z 21/10 A3008 RMK AO2 SLP178 T02110100 51011 $"
## [3] "KLNK 261554Z 05006KT 27/18 A3013 RMK AO2 SLP190 T02670178 RVRNO $"
## [4] "KLNK 161554Z 09009KT 10SM 26/19 A3007 RMK AO2 SLP171 T02610194 $"
## [5] "KLNK 211954Z 25008KT 210V270 10SM 27/10 A3005 RMK AO2 SLP170 T02720100 $"
## [6] "KLNK 050854Z 00000KT 1/2SM R36/3000VP6000FT FG 09/09 A2978 RMK AO2 SLP081 T00940094 51008 $"
##
## *** Dimensions for the cloud matrix ***
## [1] 8813 11
## List of 2
## $ tblClouds:Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 7 variables:
## ..$ isCLR : num [1:8813] 0 0 0 0 0 0 0 0 0 1 ...
## ..$ isVV : num [1:8813] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ htVV : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ numFEW: int [1:8813] 0 0 0 0 0 0 0 0 1 0 ...
## ..$ numSCT: int [1:8813] 0 0 0 0 1 0 0 0 0 0 ...
## ..$ numBKN: int [1:8813] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numOVC: int [1:8813] 1 1 1 1 1 1 1 1 0 0 ...
## $ mtxCloud : chr [1:8813, 1:11] "" "" "" "" ...
##
## *** Lowest clouds by type tibble ***
## # A tibble: 8,813 x 5
## lowVV lowOVC lowBKN lowSCT lowFEW
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NA 2800 NA NA NA
## 2 NA 2700 NA NA NA
## 3 NA 2600 NA NA NA
## 4 NA 2700 NA NA NA
## 5 NA 2700 NA 2100 NA
## 6 NA 2700 NA NA NA
## 7 NA 2700 NA NA NA
## 8 NA 2700 NA NA NA
## 9 NA NA NA NA 2600
## 10 NA NA NA NA NA
## # ... with 8,803 more rows
## List of 3
## $ lowCloud :Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 5 variables:
## ..$ lowVV : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowOVC: num [1:8813] 2800 2700 2600 2700 2700 2700 2700 2700 NA NA ...
## ..$ lowBKN: num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowSCT: num [1:8813] NA NA NA NA 2100 NA NA NA NA NA ...
## ..$ lowFEW: num [1:8813] NA NA NA NA NA NA NA NA 2600 NA ...
## $ minCeilingLevel: num [1:8813] 2800 2700 2600 2700 2700 ...
## $ minCloudLevel : num [1:8813] 2800 2700 2600 2700 2100 ...
## Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 30 variables:
## $ METAR : chr "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ WindDir : chr "300" "000" "000" "280" ...
## $ WindSpeed : int 5 0 0 3 5 9 0 3 0 0 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 10 10 10 10 10 10 10 10 10 10 ...
## $ TempC : int -3 -3 -3 -3 -3 -6 -7 -8 -10 -9 ...
## $ DewC : int -7 -7 -7 -6 -7 -10 -11 -11 -12 -12 ...
## $ Altimeter : int 3029 3030 3030 3031 3033 3033 3034 3034 3034 3035 ...
## $ SLP : int 275 277 277 281 286 289 290 291 295 295 ...
## $ FahrC : chr "T10281067" "T10331067" "T10281067" "T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## $ origMETAR : chr "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ TempF : num 27 26.1 27 27 27 ...
## $ DewF : num 19.9 19.9 19.9 21 19.9 ...
## $ modSLP : num 1028 1028 1028 1028 1029 ...
## $ isCLR : num 0 0 0 0 0 0 0 0 0 1 ...
## $ isVV : num 0 0 0 0 0 0 0 0 0 0 ...
## $ htVV : num NA NA NA NA NA NA NA NA NA NA ...
## $ numFEW : int 0 0 0 0 0 0 0 0 1 0 ...
## $ numSCT : int 0 0 0 0 1 0 0 0 0 0 ...
## $ numBKN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ numOVC : int 1 1 1 1 1 1 1 1 0 0 ...
## $ lowVV : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowOVC : num 2800 2700 2600 2700 2700 2700 2700 2700 NA NA ...
## $ lowBKN : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowSCT : num NA NA NA NA 2100 NA NA NA NA NA ...
## $ lowFEW : num NA NA NA NA NA NA NA NA 2600 NA ...
## $ wType : Factor w/ 7 levels "VV","OVC","BKN",..: 2 2 2 2 2 2 2 2 5 6 ...
## $ month : Factor w/ 12 levels "Jan","Feb","Mar",..: 12 12 12 12 12 12 12 12 12 12 ...
str(klnk2016METAR)## List of 8
## $ fullMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 30 variables:
## ..$ METAR : chr [1:8813] "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## ..$ WindDir : chr [1:8813] "300" "000" "000" "280" ...
## ..$ WindSpeed : int [1:8813] 5 0 0 3 5 9 0 3 0 0 ...
## ..$ WindGust : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ Dummy : chr [1:8813] " " " " " " " " ...
## ..$ Visibility: num [1:8813] 10 10 10 10 10 10 10 10 10 10 ...
## ..$ TempC : int [1:8813] -3 -3 -3 -3 -3 -6 -7 -8 -10 -9 ...
## ..$ DewC : int [1:8813] -7 -7 -7 -6 -7 -10 -11 -11 -12 -12 ...
## ..$ Altimeter : int [1:8813] 3029 3030 3030 3031 3033 3033 3034 3034 3034 3035 ...
## ..$ SLP : int [1:8813] 275 277 277 281 286 289 290 291 295 295 ...
## ..$ FahrC : chr [1:8813] "T10281067" "T10331067" "T10281067" "T10281061" ...
## ..$ dtime : POSIXct[1:8813], format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## ..$ origMETAR : chr [1:8813] "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## ..$ TempF : num [1:8813] 27 26.1 27 27 27 ...
## ..$ DewF : num [1:8813] 19.9 19.9 19.9 21 19.9 ...
## ..$ modSLP : num [1:8813] 1028 1028 1028 1028 1029 ...
## ..$ isCLR : num [1:8813] 0 0 0 0 0 0 0 0 0 1 ...
## ..$ isVV : num [1:8813] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ htVV : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ numFEW : int [1:8813] 0 0 0 0 0 0 0 0 1 0 ...
## ..$ numSCT : int [1:8813] 0 0 0 0 1 0 0 0 0 0 ...
## ..$ numBKN : int [1:8813] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numOVC : int [1:8813] 1 1 1 1 1 1 1 1 0 0 ...
## ..$ lowVV : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowOVC : num [1:8813] 2800 2700 2600 2700 2700 2700 2700 2700 NA NA ...
## ..$ lowBKN : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowSCT : num [1:8813] NA NA NA NA 2100 NA NA NA NA NA ...
## ..$ lowFEW : num [1:8813] NA NA NA NA NA NA NA NA 2600 NA ...
## ..$ wType : Factor w/ 7 levels "VV","OVC","BKN",..: 2 2 2 2 2 2 2 2 5 6 ...
## ..$ month : Factor w/ 12 levels "Jan","Feb","Mar",..: 12 12 12 12 12 12 12 12 12 12 ...
## $ funcMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 29 variables:
## ..$ station : chr [1:8813] "LNK" "LNK" "LNK" "LNK" ...
## ..$ valid : POSIXct[1:8813], format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## ..$ tmpf : num [1:8813] 27 26.1 27 27 27 ...
## ..$ dwpf : num [1:8813] 19.9 19.9 19.9 21 19.9 ...
## ..$ relh : num [1:8813] 74.5 77.3 74.5 78 74.5 ...
## ..$ drct : num [1:8813] 300 0 0 280 310 10 0 10 0 0 ...
## ..$ sknt : num [1:8813] 5 0 0 3 5 9 0 3 0 0 ...
## ..$ p01i : chr [1:8813] "0.00" "0.00" "0.00" "0.00" ...
## ..$ alti : num [1:8813] 30.3 30.3 30.3 30.3 30.3 ...
## ..$ mslp : num [1:8813] 1028 1028 1028 1028 1029 ...
## ..$ vsby : num [1:8813] 10 10 10 10 10 10 10 10 10 10 ...
## ..$ gust : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyc1 : chr [1:8813] "OVC" "OVC" "OVC" "OVC" ...
## ..$ skyc2 : chr [1:8813] NA NA NA NA ...
## ..$ skyc3 : chr [1:8813] NA NA NA NA ...
## ..$ skyc4 : logi [1:8813] NA NA NA NA NA NA ...
## ..$ skyl1 : num [1:8813] 2800 2700 2600 2700 2100 2700 2700 2700 2600 NA ...
## ..$ skyl2 : num [1:8813] NA NA NA NA 2700 NA NA NA NA NA ...
## ..$ skyl3 : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl4 : logi [1:8813] NA NA NA NA NA NA ...
## ..$ wxcodes : chr [1:8813] NA NA NA NA ...
## ..$ ice_accretion_1hr: chr [1:8813] NA NA NA NA ...
## ..$ ice_accretion_3hr: chr [1:8813] NA NA NA NA ...
## ..$ ice_accretion_6hr: chr [1:8813] NA NA NA NA ...
## ..$ peak_wind_gust : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_drct : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_time : POSIXct[1:8813], format: NA NA ...
## ..$ feel : num [1:8813] 20.4 26.1 27 22.9 20.4 ...
## ..$ metar : chr [1:8813] "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## ..- attr(*, "spec")=
## .. .. cols(
## .. .. station = col_character(),
## .. .. valid = col_datetime(format = ""),
## .. .. tmpf = col_double(),
## .. .. dwpf = col_double(),
## .. .. relh = col_double(),
## .. .. drct = col_double(),
## .. .. sknt = col_double(),
## .. .. p01i = col_character(),
## .. .. alti = col_double(),
## .. .. mslp = col_double(),
## .. .. vsby = col_double(),
## .. .. gust = col_double(),
## .. .. skyc1 = col_character(),
## .. .. skyc2 = col_character(),
## .. .. skyc3 = col_character(),
## .. .. skyc4 = col_logical(),
## .. .. skyl1 = col_double(),
## .. .. skyl2 = col_double(),
## .. .. skyl3 = col_double(),
## .. .. skyl4 = col_logical(),
## .. .. wxcodes = col_character(),
## .. .. ice_accretion_1hr = col_character(),
## .. .. ice_accretion_3hr = col_character(),
## .. .. ice_accretion_6hr = col_character(),
## .. .. peak_wind_gust = col_double(),
## .. .. peak_wind_drct = col_double(),
## .. .. peak_wind_time = col_datetime(format = ""),
## .. .. feel = col_double(),
## .. .. metar = col_character()
## .. .. )
## $ windMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 32 variables:
## ..$ station : chr [1:8813] "LNK" "LNK" "LNK" "LNK" ...
## ..$ valid : POSIXct[1:8813], format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## ..$ tmpf : num [1:8813] 27 26.1 27 27 27 ...
## ..$ dwpf : num [1:8813] 19.9 19.9 19.9 21 19.9 ...
## ..$ relh : num [1:8813] 74.5 77.3 74.5 78 74.5 ...
## ..$ drct : num [1:8813] 300 0 0 280 310 10 0 10 0 0 ...
## ..$ sknt : num [1:8813] 5 0 0 3 5 9 0 3 0 0 ...
## ..$ p01i : chr [1:8813] "0.00" "0.00" "0.00" "0.00" ...
## ..$ alti : num [1:8813] 30.3 30.3 30.3 30.3 30.3 ...
## ..$ mslp : num [1:8813] 1028 1028 1028 1028 1029 ...
## ..$ vsby : num [1:8813] 10 10 10 10 10 10 10 10 10 10 ...
## ..$ gust : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyc1 : chr [1:8813] "OVC" "OVC" "OVC" "OVC" ...
## ..$ skyc2 : chr [1:8813] NA NA NA NA ...
## ..$ skyc3 : chr [1:8813] NA NA NA NA ...
## ..$ skyc4 : logi [1:8813] NA NA NA NA NA NA ...
## ..$ skyl1 : num [1:8813] 2800 2700 2600 2700 2100 2700 2700 2700 2600 NA ...
## ..$ skyl2 : num [1:8813] NA NA NA NA 2700 NA NA NA NA NA ...
## ..$ skyl3 : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl4 : logi [1:8813] NA NA NA NA NA NA ...
## ..$ wxcodes : chr [1:8813] NA NA NA NA ...
## ..$ ice_accretion_1hr: chr [1:8813] NA NA NA NA ...
## ..$ ice_accretion_3hr: chr [1:8813] NA NA NA NA ...
## ..$ ice_accretion_6hr: chr [1:8813] NA NA NA NA ...
## ..$ peak_wind_gust : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_drct : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_time : POSIXct[1:8813], format: NA NA ...
## ..$ feel : num [1:8813] 20.4 26.1 27 22.9 20.4 ...
## ..$ metar : chr [1:8813] "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## ..$ dirW : chr [1:8813] "300" "000" "000" "280" ...
## ..$ spdW : num [1:8813] 5 0 0 3 5 9 0 3 0 0 ...
## ..$ gustW : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## $ initMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 13 variables:
## ..$ METAR : chr [1:8813] "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## ..$ WindDir : chr [1:8813] "300" "000" "000" "280" ...
## ..$ WindSpeed : chr [1:8813] "05" "00" "00" "03" ...
## ..$ WindGust : chr [1:8813] NA NA NA NA ...
## ..$ Dummy : chr [1:8813] " " " " " " " " ...
## ..$ Visibility: chr [1:8813] "10SM" "10SM" "10SM" "10SM" ...
## ..$ TempC : chr [1:8813] "M03" "M03" "M03" "M03" ...
## ..$ DewC : chr [1:8813] "M07" "M07" "M07" "M06" ...
## ..$ Altimeter : chr [1:8813] "A3029" "A3030" "A3030" "A3031" ...
## ..$ SLP : chr [1:8813] "SLP275" "SLP277" "SLP277" "SLP281" ...
## ..$ FahrC : chr [1:8813] "T10281067" "T10331067" "T10281067" "T10281061" ...
## ..$ dtime : POSIXct[1:8813], format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## ..$ origMETAR : chr [1:8813] "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## $ convMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 15 variables:
## ..$ METAR : chr [1:8813] "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## ..$ WindDir : chr [1:8813] "300" "000" "000" "280" ...
## ..$ WindSpeed : int [1:8813] 5 0 0 3 5 9 0 3 0 0 ...
## ..$ WindGust : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ Dummy : chr [1:8813] " " " " " " " " ...
## ..$ Visibility: num [1:8813] 10 10 10 10 10 10 10 10 10 10 ...
## ..$ TempC : int [1:8813] -3 -3 -3 -3 -3 -6 -7 -8 -10 -9 ...
## ..$ DewC : int [1:8813] -7 -7 -7 -6 -7 -10 -11 -11 -12 -12 ...
## ..$ Altimeter : int [1:8813] 3029 3030 3030 3031 3033 3033 3034 3034 3034 3035 ...
## ..$ SLP : int [1:8813] 275 277 277 281 286 289 290 291 295 295 ...
## ..$ FahrC : chr [1:8813] "T10281067" "T10331067" "T10281067" "T10281061" ...
## ..$ dtime : POSIXct[1:8813], format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## ..$ origMETAR : chr [1:8813] "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## ..$ TempF : num [1:8813] 27 26.1 27 27 27 ...
## ..$ DewF : num [1:8813] 19.9 19.9 19.9 21 19.9 ...
## $ parseMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 16 variables:
## ..$ METAR : chr [1:8813] "54Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "54Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "54Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067" "54Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## ..$ WindDir : chr [1:8813] "300" "000" "000" "280" ...
## ..$ WindSpeed : int [1:8813] 5 0 0 3 5 9 0 3 0 0 ...
## ..$ WindGust : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## ..$ Dummy : chr [1:8813] " " " " " " " " ...
## ..$ Visibility: num [1:8813] 10 10 10 10 10 10 10 10 10 10 ...
## ..$ TempC : int [1:8813] -3 -3 -3 -3 -3 -6 -7 -8 -10 -9 ...
## ..$ DewC : int [1:8813] -7 -7 -7 -6 -7 -10 -11 -11 -12 -12 ...
## ..$ Altimeter : int [1:8813] 3029 3030 3030 3031 3033 3033 3034 3034 3034 3035 ...
## ..$ SLP : int [1:8813] 275 277 277 281 286 289 290 291 295 295 ...
## ..$ FahrC : chr [1:8813] "T10281067" "T10331067" "T10281067" "T10281061" ...
## ..$ dtime : POSIXct[1:8813], format: "2015-12-31 00:54:00" "2015-12-31 01:54:00" ...
## ..$ origMETAR : chr [1:8813] "KLNK 310054Z 30005KT 10SM OVC028 M03/M07 A3029 RMK AO2 SLP275 T10281067" "KLNK 310154Z 00000KT 10SM OVC027 M03/M07 A3030 RMK AO2 SLP277 T10331067" "KLNK 310254Z 00000KT 10SM OVC026 M03/M07 A3030 RMK AO2 SLP277 T10281067 51008" "KLNK 310354Z 28003KT 10SM OVC027 M03/M06 A3031 RMK AO2 SLP281 T10281061" ...
## ..$ TempF : num [1:8813] 27 26.1 27 27 27 ...
## ..$ DewF : num [1:8813] 19.9 19.9 19.9 21 19.9 ...
## ..$ modSLP : num [1:8813] 1028 1028 1028 1028 1029 ...
## $ initClouds :List of 2
## ..$ tblClouds:Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 7 variables:
## .. ..$ isCLR : num [1:8813] 0 0 0 0 0 0 0 0 0 1 ...
## .. ..$ isVV : num [1:8813] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ htVV : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ numFEW: int [1:8813] 0 0 0 0 0 0 0 0 1 0 ...
## .. ..$ numSCT: int [1:8813] 0 0 0 0 1 0 0 0 0 0 ...
## .. ..$ numBKN: int [1:8813] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ numOVC: int [1:8813] 1 1 1 1 1 1 1 1 0 0 ...
## ..$ mtxCloud : chr [1:8813, 1:11] "" "" "" "" ...
## $ processedClouds:List of 3
## ..$ lowCloud :Classes 'tbl_df', 'tbl' and 'data.frame': 8813 obs. of 5 variables:
## .. ..$ lowVV : num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ lowOVC: num [1:8813] 2800 2700 2600 2700 2700 2700 2700 2700 NA NA ...
## .. ..$ lowBKN: num [1:8813] NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ lowSCT: num [1:8813] NA NA NA NA 2100 NA NA NA NA NA ...
## .. ..$ lowFEW: num [1:8813] NA NA NA NA NA NA NA NA 2600 NA ...
## ..$ minCeilingLevel: num [1:8813] 2800 2700 2600 2700 2700 ...
## ..$ minCloudLevel : num [1:8813] 2800 2700 2600 2700 2100 ...Example #23: Running for a Different Station
The functions can be run for a different station.
Example code includes:
# Set key parameters for reading and interpreting METAR
fname <- "./RInputFiles/metar_kord_2016.txt" # file name for raw METAR data
timeZ <- "51Z" # Zulu time that METAR is recorded at this station
expMin <- as.POSIXct("2015-12-31 00:51:00", tz="UTC") # Expected first time read
expDays <- 368 # Expected total days read
locMET <- "Chicago, IL" # Description of city or location
shortMET <- "KORD METAR (2016)" # Station code and timing
longMET <- "Chicago, IL O'Hare Hourly METAR (2016)" # Description of city or location and timing
# Extraction format for METAR - paste the expected Zulu time at the front
valMet <- paste0(timeZ, ".*?(VRB|\\d{3})(\\d{2})(G\\d{2})?KT(.*?)(\\d{1,2}SM).*?\\s(M?\\d{2})/(M?\\d{2}).*?(A\\d{4}).*?RMK.*?(SLP\\d{3}).*?(T\\d{8})")
# Run the process for Lincoln, NE
kord2016METAR <- runAllMETAR(fname=fname, timeZ=timeZ, expMin=expMin, expDays=expDays,
locMET=locMET, shortMET=shortMET, longMET=longMET, valMet=valMet
)## Parsed with column specification:
## cols(
## .default = col_double(),
## station = col_character(),
## valid = col_datetime(format = ""),
## p01i = col_character(),
## skyc1 = col_character(),
## skyc2 = col_character(),
## skyc3 = col_character(),
## skyc4 = col_character(),
## wxcodes = col_character(),
## ice_accretion_1hr = col_logical(),
## ice_accretion_3hr = col_logical(),
## ice_accretion_6hr = col_logical(),
## peak_wind_time = col_datetime(format = ""),
## metar = col_character()
## )## See spec(...) for full column specifications.## Warning: 22 parsing failures.
## row col expected actual file
## 1678 ice_accretion_1hr 1/0/T/F/TRUE/FALSE 0.01 './RInputFiles/metar_kord_2016.txt'
## 1680 ice_accretion_6hr 1/0/T/F/TRUE/FALSE 0.01 './RInputFiles/metar_kord_2016.txt'
## 1902 ice_accretion_1hr 1/0/T/F/TRUE/FALSE 0.01 './RInputFiles/metar_kord_2016.txt'
## 1903 ice_accretion_1hr 1/0/T/F/TRUE/FALSE 0.01 './RInputFiles/metar_kord_2016.txt'
## 1903 ice_accretion_6hr 1/0/T/F/TRUE/FALSE 0.01 './RInputFiles/metar_kord_2016.txt'
## .... ................. .................. ...... ...................................
## See problems(...) for more details.## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 10809 obs. of 29 variables:
## $ station : chr "ORD" "ORD" "ORD" "ORD" ...
## $ valid : POSIXct, format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## $ tmpf : num 27 27 27 27 27 ...
## $ dwpf : num 21.9 21.9 21 21 21 ...
## $ relh : num 81 81 78 78 78 ...
## $ drct : num 260 260 250 240 250 250 270 260 250 250 ...
## $ sknt : num 8 8 8 9 6 7 10 11 8 10 ...
## $ p01i : chr "0.00" "0.00" "0.00" "0.00" ...
## $ alti : num 30.2 30.2 30.2 30.2 30.2 ...
## $ mslp : num 1024 1024 1024 1024 1024 ...
## $ vsby : num 8 9 10 10 10 10 10 10 10 10 ...
## $ gust : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr "OVC" "OVC" "OVC" "OVC" ...
## $ skyc2 : chr NA NA NA NA ...
## $ skyc3 : chr NA NA NA NA ...
## $ skyc4 : chr NA NA NA NA ...
## $ skyl1 : num 1600 1600 1500 1900 1700 1700 1600 1500 2000 1600 ...
## $ skyl2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyl3 : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyl4 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr NA NA NA NA ...
## $ ice_accretion_1hr: logi NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi NA NA NA NA NA NA ...
## $ peak_wind_gust : num NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_drct : num NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_time : POSIXct, format: NA NA ...
## $ feel : num 17.9 17.9 17.9 17.2 19.5 ...
## $ metar : chr "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
##
## *** OBSERVATIONS EXPECTED BUT NOT RECORDED ***
## [1] "2016-04-09 03:51:00 UTC" "2016-06-11 20:51:00 UTC"
## [3] "2016-06-17 23:51:00 UTC" "2016-06-18 00:51:00 UTC"
## [5] "2016-06-30 15:51:00 UTC" "2016-07-13 14:51:00 UTC"
## [7] "2016-07-13 15:51:00 UTC" "2016-07-13 16:51:00 UTC"
## [9] "2016-07-13 17:51:00 UTC" "2016-07-30 01:51:00 UTC"
## [11] "2016-08-05 07:51:00 UTC" "2016-08-07 14:51:00 UTC"
## [13] "2016-11-21 00:51:00 UTC" "2016-11-23 09:51:00 UTC"
## [15] "2016-11-27 18:51:00 UTC" "2016-12-01 11:51:00 UTC"
## [17] "2016-12-05 18:51:00 UTC"
##
## *** OBSERVATIONS RECORDED BUT NOT EXPECTED ***
## POSIXct of length 0
##
## *** Are the extracted records unique? ***
## [1] TRUE
##
##
## *** First 6 winds and parsing ***
## [,1] [,2] [,3] [,4]
## [1,] "26008KT" "260" "08" NA
## [2,] "26008KT" "260" "08" NA
## [3,] "25008KT" "250" "08" NA
## [4,] "24009KT" "240" "09" NA
## [5,] "25006KT" "250" "06" NA
## [6,] "25007KT" "250" "07" NA
##
## *** Table of WIND DIRECTION ***
##
## 000 010 020 030 040 050 060 070 080 090 100 110 120 130 140 150
## 512 183 170 247 241 210 181 170 134 197 142 77 74 116 168 157
## 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310
## 181 269 307 333 365 337 219 280 318 344 369 342 330 282 240 196
## 320 330 340 350 360 VRB <NA>
## 206 216 196 197 171 137 1
##
## *** Table of WIND SPEED ***
##
## 00 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17
## 512 531 635 741 821 817 775 775 624 519 473 424 316 233 172 127
## 18 19 20 21 22 23 24 25 26 27 28 29 30 36 37 <NA>
## 93 64 53 35 22 17 15 6 5 1 4 1 1 1 1 1
##
## *** Table of WIND GUST ***
##
## G14 G15 G16 G17 G18 G19 G20 G21 G22 G23 G24 G25 G26 G27 G28 G29
## 5 11 40 44 80 119 109 132 128 97 88 84 71 46 36 30
## G30 G31 G32 G33 G34 G35 G36 G37 G38 G39 G40 G41 G42 G43 G44 G45
## 27 18 19 15 9 5 3 3 3 2 3 1 1 1 2 2
## G46 G52 G54 <NA>
## 2 1 1 7577
##
## *** WIND DATA WAS NOT CAPTURED FROM: ***
## # A tibble: 1 x 1
## metar
## <chr>
## 1 KORD 061851Z 19012G19 10SM BKN047 BKN250 32/23 A3003 RMK SLP163 T03220228
## Warning: Removed 1 rows containing non-finite values (stat_count).
## Warning: Removed 2 rows containing missing values (geom_point).##
## *** Tentative Summary of Element Parsing ***
## .
## FALSE TRUE
## 10 8805
##
## *** Data Not Matched ***
## [1] "KORD 100151Z 34014KT 4SM -SN BR BKN008 BKN016 OVC022 00/M01 A2976 RMK T00001011"
## [2] "KORD 100251Z 34016KT 3SM -SN BR OVC011 M01/M02 A2975 RMK T10111022"
## [3] "KORD 071551Z 00000KT 10SM SCT030 SCT060 BKN250 27/15 A3007"
## [4] "KORD 071651Z 13005KT 10SM FEW030 BKN060 BKN250 28/17 A3007"
## [5] "KORD 071751Z 10005KT 10SM SCT048 BKN220 BKN250 30/16 3006"
## [6] "KORD 071851Z 10009KT 10SM SCT050 BKN230 BKN250 31/14 A3005"
## [7] "KORD 061851Z 19012G19 10SM BKN047 BKN250 32/23 A3003 RMK SLP163 T03220228"
## [8] "KORD 261151Z 00000KT 10SM OVC029 02/M01 A3034"
## [9] "KORD 280351Z 18012KT 10SM BKN075 OVC100 06/02 A2976"
## [10] "KORD 210451Z 25009KT 10SM BKN250 M04/M08 A3014"
##
## *** Parsing matrix summary ***
## [1] 8815 11
## [,1]
## [1,] "51Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056"
## [2,] "51Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056"
## [3,] "51Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061"
## [4,] "51Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061"
## [5,] "51Z 25006KT 10SM OVC017 M03/M06 A3021 RMK AO2 SLP239 T10281061"
## [6,] "51Z 25007KT 10SM OVC017 M03/M06 A3020 RMK AO2 SLP237 4/002 T10281061"
## [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
## [1,] "260" "08" NA " " "8SM" "M03" "M06" "A3021" "SLP239" "T10281056"
## [2,] "260" "08" NA " " "9SM" "M03" "M06" "A3021" "SLP240" "T10281056"
## [3,] "250" "08" NA " " "10SM" "M03" "M06" "A3021" "SLP239" "T10281061"
## [4,] "240" "09" NA " " "10SM" "M03" "M06" "A3022" "SLP242" "T10281061"
## [5,] "250" "06" NA " " "10SM" "M03" "M06" "A3021" "SLP239" "T10281061"
## [6,] "250" "07" NA " " "10SM" "M03" "M06" "A3020" "SLP237" "T10281061"
##
## *** Summary of the parsed data ***
## Observations: 8,815
## Variables: 13
## $ METAR <chr> "51Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10...
## $ WindDir <chr> "260", "260", "250", "240", "250", "250", "270", "260", ...
## $ WindSpeed <chr> "08", "08", "08", "09", "06", "07", "10", "11", "08", "1...
## $ WindGust <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ Dummy <chr> " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "...
## $ Visibility <chr> "8SM", "9SM", "10SM", "10SM", "10SM", "10SM", "10SM", "1...
## $ TempC <chr> "M03", "M03", "M03", "M03", "M03", "M03", "M03", "M03", ...
## $ DewC <chr> "M06", "M06", "M06", "M06", "M06", "M06", "M06", "M06", ...
## $ Altimeter <chr> "A3021", "A3021", "A3021", "A3022", "A3021", "A3020", "A...
## $ SLP <chr> "SLP239", "SLP240", "SLP239", "SLP242", "SLP239", "SLP23...
## $ FahrC <chr> "T10281056", "T10281056", "T10281061", "T10281061", "T10...
## $ dtime <dttm> 2015-12-31 00:51:00, 2015-12-31 01:51:00, 2015-12-31 02...
## $ origMETAR <chr> "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 S...## Warning: NAs introduced by coercion
##
## *** Parsed data structure, head, tail, and random sample ***
## Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 15 variables:
## $ METAR : chr "51Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "51Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "51Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061" "51Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ WindDir : chr "260" "260" "250" "240" ...
## $ WindSpeed : int 8 8 8 9 6 7 10 11 8 10 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 8 9 10 10 10 10 10 10 10 10 ...
## $ TempC : int -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 ...
## $ DewC : int -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 ...
## $ Altimeter : int 3021 3021 3021 3022 3021 3020 3021 3022 3022 3021 ...
## $ SLP : int 239 240 239 242 239 237 237 242 244 239 ...
## $ FahrC : chr "T10281056" "T10281056" "T10281061" "T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## $ origMETAR : chr "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ TempF : num 27 27 27 27 27 ...
## $ DewF : num 21.9 21.9 21 21 21 ...
## # A tibble: 6 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 51Z ~ 260 8 NA " " 8 -3 -6 3021 239
## 2 51Z ~ 260 8 NA " " 9 -3 -6 3021 240
## 3 51Z ~ 250 8 NA " " 10 -3 -6 3021 239
## 4 51Z ~ 240 9 NA " " 10 -3 -6 3022 242
## 5 51Z ~ 250 6 NA " " 10 -3 -6 3021 239
## 6 51Z ~ 250 7 NA " " 10 -3 -6 3020 237
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
## # A tibble: 6 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 51Z ~ 200 8 NA " " 10 3 -7 3010 202
## 2 51Z ~ 230 8 NA " " 10 3 -7 3011 204
## 3 51Z ~ 200 6 NA " " 10 4 -7 3011 203
## 4 51Z ~ 000 0 NA " " 10 3 -8 3009 199
## 5 51Z ~ 000 0 NA " " 10 1 -7 3012 208
## 6 51Z ~ 000 0 NA " " 10 1 -7 3014 217
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
## # A tibble: 20 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 51Z ~ 090 9 NA " " 10 21 19 2982 93
## 2 51Z ~ 230 7 NA " " 10 27 9 2982 97
## 3 51Z ~ 260 12 NA " " 10 -4 -8 3001 169
## 4 51Z ~ 180 8 NA " " 10 16 11 2979 83
## 5 51Z ~ 260 18 NA " " 10 -9 -15 3011 211
## 6 51Z ~ 040 7 NA " " 4 20 19 2987 111
## 7 51Z ~ 200 12 NA " " 10 26 16 3018 216
## 8 51Z ~ 250 4 NA " " 10 -3 -7 3014 215
## 9 51Z ~ 260 6 NA " " 10 19 17 2983 96
## 10 51Z ~ 140 8 NA " " 10 19 14 3009 188
## 11 51Z ~ 040 4 NA " " 10 18 14 3024 236
## 12 51Z ~ 050 8 NA " " 10 16 7 3020 227
## 13 51Z ~ 290 10 NA " " 10 1 -4 3029 263
## 14 51Z ~ 340 6 NA " " 8 15 14 2999 156
## 15 51Z ~ 290 10 NA " " 10 -18 -23 3036 298
## 16 51Z ~ 260 8 NA " " 10 18 16 2964 33
## 17 51Z ~ 030 8 NA " " 10 7 3 3003 172
## 18 51Z ~ 130 8 NA " 1 ~ 2 3 3 2981 100
## 19 51Z ~ 260 7 NA " " 10 -3 -7 3034 281
## 20 51Z ~ 170 8 NA " " 10 21 14 2987 110
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
##
## *** Number of NA values ***
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC
## 10 10 10 8815 10 10 10
## DewC Altimeter SLP FahrC dtime origMETAR TempF
## 10 10 10 10 0 0 10
## DewF
## 10
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(diff(sort(x))): no non-missing arguments to min; returning Inf## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## # A tibble: 20 x 2
## Visibility n
## <dbl> <int>
## 1 0 3
## 2 0.125 8
## 3 0.25 22
## 4 0.5 21
## 5 0.75 19
## 6 1 27
## 7 1.25 22
## 8 1.5 34
## 9 1.75 30
## 10 2 64
## 11 2.5 54
## 12 3 99
## 13 4 108
## 14 5 168
## 15 6 169
## 16 7 179
## 17 8 184
## 18 9 284
## 19 10 7310
## 20 NA 10
## WindGust n
## 1 14 5
## 2 15 10
## 3 16 39
## 4 17 44
## 5 18 80
## 6 19 119
## 7 20 109
## 8 21 132
## 9 22 127
## 10 23 97
## 11 24 88
## 12 25 84
## 13 26 71
## 14 27 46
## 15 28 36
## 16 29 30
## 17 30 27
## 18 31 18
## 19 32 19
## 20 33 15
## 21 34 9
## 22 35 5
## 23 36 3
## 24 37 3
## 25 38 3
## 26 39 2
## 27 40 3
## 28 41 1
## 29 42 1
## 30 43 1
## 31 44 2
## 32 45 2
## 33 46 2
## 34 52 1
## 35 54 1
## 36 NA 7580## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
##
## *** Correlations use 8805 complete cases (99.9% of 8815 total) ***
## TempC TempF DewC DewF Altimeter modSLP WindSpeed Visibility
## TempC 1.00 1.00 0.93 0.93 -0.22 -0.29 -0.12 0.19
## TempF 1.00 1.00 0.93 0.93 -0.22 -0.29 -0.12 0.19
## DewC 0.93 0.93 1.00 1.00 -0.27 -0.34 -0.19 0.05
## DewF 0.93 0.93 1.00 1.00 -0.28 -0.34 -0.19 0.05
## Altimeter -0.22 -0.22 -0.27 -0.28 1.00 1.00 -0.31 0.19
## modSLP -0.29 -0.29 -0.34 -0.34 1.00 1.00 -0.29 0.17
## WindSpeed -0.12 -0.12 -0.19 -0.19 -0.31 -0.29 1.00 0.01
## Visibility 0.19 0.19 0.05 0.05 0.19 0.17 0.01 1.00
## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
##
## *** Regression call is: modSLP ~ Altimeter ***
##
## Call:
## lm(formula = formula(myChar), data = met)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.31041 -0.47561 -0.07852 0.43888 1.66927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.312e+01 8.598e-01 -26.89 <2e-16 ***
## Altimeter 3.464e-01 2.864e-04 1209.37 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5573 on 8803 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.994, Adjusted R-squared: 0.994
## F-statistic: 1.463e+06 on 1 and 8803 DF, p-value: < 2.2e-16## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
##
## *** Regression call is: modSLP ~ Altimeter + TempF ***
##
## Call:
## lm(formula = formula(myChar), data = met)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.69460 -0.12269 0.00055 0.12142 0.73628
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.603e+00 2.864e-01 -16.07 <2e-16 ***
## Altimeter 3.407e-01 9.502e-05 3585.30 <2e-16 ***
## TempF -2.606e-02 9.503e-05 -274.26 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1804 on 8802 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.9994, Adjusted R-squared: 0.9994
## F-statistic: 7.018e+06 on 2 and 8802 DF, p-value: < 2.2e-16## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
##
## *** Counts by number of layers of each cloud type ***
## isCLR isVV numFEW numSCT numBKN numOVC n
## 1 0 0 0 0 0 0 1
## 2 0 0 0 0 0 1 1404
## 3 0 0 0 0 1 0 515
## 4 0 0 0 0 1 1 551
## 5 0 0 0 0 2 0 236
## 6 0 0 0 0 2 1 142
## 7 0 0 0 0 3 0 49
## 8 0 0 0 0 3 1 9
## 9 0 0 0 0 4 0 4
## 10 0 0 0 1 0 0 381
## 11 0 0 0 1 0 1 144
## 12 0 0 0 1 1 0 234
## 13 0 0 0 1 1 1 193
## 14 0 0 0 1 2 0 114
## 15 0 0 0 1 2 1 21
## 16 0 0 0 1 3 0 21
## 17 0 0 0 1 3 1 1
## 18 0 0 0 2 0 0 173
## 19 0 0 0 2 0 1 16
## 20 0 0 0 2 1 0 61
## 21 0 0 0 2 1 1 14
## 22 0 0 0 2 2 0 14
## 23 0 0 0 3 0 0 17
## 24 0 0 1 0 0 0 1050
## 25 0 0 1 0 0 1 240
## 26 0 0 1 0 1 0 359
## 27 0 0 1 0 1 1 217
## 28 0 0 1 0 2 0 130
## 29 0 0 1 0 2 1 19
## 30 0 0 1 0 3 0 14
## 31 0 0 1 1 0 0 323
## 32 0 0 1 1 0 1 105
## 33 0 0 1 1 1 0 226
## 34 0 0 1 1 1 1 39
## 35 0 0 1 1 2 0 54
## 36 0 0 1 2 0 0 65
## 37 0 0 1 2 0 1 3
## 38 0 0 1 2 1 0 17
## 39 0 0 1 2 1 1 1
## 40 0 0 1 2 2 0 2
## 41 0 0 1 3 0 0 2
## 42 0 0 2 0 0 0 431
## 43 0 0 2 0 0 1 37
## 44 0 0 2 0 1 0 109
## 45 0 0 2 0 1 1 17
## 46 0 0 2 0 2 0 9
## 47 0 0 2 1 0 0 96
## 48 0 0 2 1 0 1 6
## 49 0 0 2 1 1 0 42
## 50 0 0 2 1 1 1 4
## 51 0 0 2 1 2 0 1
## 52 0 0 2 2 0 0 9
## 53 0 0 3 0 0 0 5
## 54 0 0 4 0 0 0 1
## 55 0 1 0 0 0 0 55
## 56 1 0 0 0 0 0 812
##
## *** METAR records where no clouds were extracted ***
## [1] "KORD 071951Z AUTO 07005KT 10SM 28/14 A3004 RMK AO2 SLP169 T02780139 $"
##
## *** Dimensions for the cloud matrix ***
## [1] 8815 14
## List of 2
## $ tblClouds:Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 7 variables:
## ..$ isCLR : num [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ isVV : num [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ htVV : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ numFEW: int [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numSCT: int [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numBKN: int [1:8815] 0 0 0 0 0 0 0 0 0 1 ...
## ..$ numOVC: int [1:8815] 1 1 1 1 1 1 1 1 1 0 ...
## $ mtxCloud : chr [1:8815, 1:14] "" "" "" "" ...
##
## *** Lowest clouds by type tibble ***
## # A tibble: 8,815 x 5
## lowVV lowOVC lowBKN lowSCT lowFEW
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NA 1600 NA NA NA
## 2 NA 1600 NA NA NA
## 3 NA 1500 NA NA NA
## 4 NA 1900 NA NA NA
## 5 NA 1700 NA NA NA
## 6 NA 1700 NA NA NA
## 7 NA 1600 NA NA NA
## 8 NA 1500 NA NA NA
## 9 NA 2000 NA NA NA
## 10 NA NA 1600 NA NA
## # ... with 8,805 more rows
## List of 3
## $ lowCloud :Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 5 variables:
## ..$ lowVV : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowOVC: num [1:8815] 1600 1600 1500 1900 1700 1700 1600 1500 2000 NA ...
## ..$ lowBKN: num [1:8815] NA NA NA NA NA NA NA NA NA 1600 ...
## ..$ lowSCT: num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowFEW: num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## $ minCeilingLevel: num [1:8815] 1600 1600 1500 1900 1700 1700 1600 1500 2000 1600 ...
## $ minCloudLevel : num [1:8815] 1600 1600 1500 1900 1700 1700 1600 1500 2000 1600 ...
## Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 30 variables:
## $ METAR : chr "51Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "51Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "51Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061" "51Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ WindDir : chr "260" "260" "250" "240" ...
## $ WindSpeed : int 8 8 8 9 6 7 10 11 8 10 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 8 9 10 10 10 10 10 10 10 10 ...
## $ TempC : int -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 ...
## $ DewC : int -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 ...
## $ Altimeter : int 3021 3021 3021 3022 3021 3020 3021 3022 3022 3021 ...
## $ SLP : int 239 240 239 242 239 237 237 242 244 239 ...
## $ FahrC : chr "T10281056" "T10281056" "T10281061" "T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## $ origMETAR : chr "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ TempF : num 27 27 27 27 27 ...
## $ DewF : num 21.9 21.9 21 21 21 ...
## $ modSLP : num 1024 1024 1024 1024 1024 ...
## $ isCLR : num 0 0 0 0 0 0 0 0 0 0 ...
## $ isVV : num 0 0 0 0 0 0 0 0 0 0 ...
## $ htVV : num NA NA NA NA NA NA NA NA NA NA ...
## $ numFEW : int 0 0 0 0 0 0 0 0 0 0 ...
## $ numSCT : int 0 0 0 0 0 0 0 0 0 0 ...
## $ numBKN : int 0 0 0 0 0 0 0 0 0 1 ...
## $ numOVC : int 1 1 1 1 1 1 1 1 1 0 ...
## $ lowVV : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowOVC : num 1600 1600 1500 1900 1700 1700 1600 1500 2000 NA ...
## $ lowBKN : num NA NA NA NA NA NA NA NA NA 1600 ...
## $ lowSCT : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowFEW : num NA NA NA NA NA NA NA NA NA NA ...
## $ wType : Factor w/ 7 levels "VV","OVC","BKN",..: 2 2 2 2 2 2 2 2 2 3 ...
## $ month : Factor w/ 12 levels "Jan","Feb","Mar",..: 12 12 12 12 12 12 12 12 12 12 ...
str(kord2016METAR)## List of 8
## $ fullMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 30 variables:
## ..$ METAR : chr [1:8815] "51Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "51Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "51Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061" "51Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## ..$ WindDir : chr [1:8815] "260" "260" "250" "240" ...
## ..$ WindSpeed : int [1:8815] 8 8 8 9 6 7 10 11 8 10 ...
## ..$ WindGust : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ Dummy : chr [1:8815] " " " " " " " " ...
## ..$ Visibility: num [1:8815] 8 9 10 10 10 10 10 10 10 10 ...
## ..$ TempC : int [1:8815] -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 ...
## ..$ DewC : int [1:8815] -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 ...
## ..$ Altimeter : int [1:8815] 3021 3021 3021 3022 3021 3020 3021 3022 3022 3021 ...
## ..$ SLP : int [1:8815] 239 240 239 242 239 237 237 242 244 239 ...
## ..$ FahrC : chr [1:8815] "T10281056" "T10281056" "T10281061" "T10281061" ...
## ..$ dtime : POSIXct[1:8815], format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## ..$ origMETAR : chr [1:8815] "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## ..$ TempF : num [1:8815] 27 27 27 27 27 ...
## ..$ DewF : num [1:8815] 21.9 21.9 21 21 21 ...
## ..$ modSLP : num [1:8815] 1024 1024 1024 1024 1024 ...
## ..$ isCLR : num [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ isVV : num [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ htVV : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ numFEW : int [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numSCT : int [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numBKN : int [1:8815] 0 0 0 0 0 0 0 0 0 1 ...
## ..$ numOVC : int [1:8815] 1 1 1 1 1 1 1 1 1 0 ...
## ..$ lowVV : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowOVC : num [1:8815] 1600 1600 1500 1900 1700 1700 1600 1500 2000 NA ...
## ..$ lowBKN : num [1:8815] NA NA NA NA NA NA NA NA NA 1600 ...
## ..$ lowSCT : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowFEW : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ wType : Factor w/ 7 levels "VV","OVC","BKN",..: 2 2 2 2 2 2 2 2 2 3 ...
## ..$ month : Factor w/ 12 levels "Jan","Feb","Mar",..: 12 12 12 12 12 12 12 12 12 12 ...
## $ funcMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 29 variables:
## ..$ station : chr [1:8815] "ORD" "ORD" "ORD" "ORD" ...
## ..$ valid : POSIXct[1:8815], format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## ..$ tmpf : num [1:8815] 27 27 27 27 27 ...
## ..$ dwpf : num [1:8815] 21.9 21.9 21 21 21 ...
## ..$ relh : num [1:8815] 81 81 78 78 78 ...
## ..$ drct : num [1:8815] 260 260 250 240 250 250 270 260 250 250 ...
## ..$ sknt : num [1:8815] 8 8 8 9 6 7 10 11 8 10 ...
## ..$ p01i : chr [1:8815] "0.00" "0.00" "0.00" "0.00" ...
## ..$ alti : num [1:8815] 30.2 30.2 30.2 30.2 30.2 ...
## ..$ mslp : num [1:8815] 1024 1024 1024 1024 1024 ...
## ..$ vsby : num [1:8815] 8 9 10 10 10 10 10 10 10 10 ...
## ..$ gust : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyc1 : chr [1:8815] "OVC" "OVC" "OVC" "OVC" ...
## ..$ skyc2 : chr [1:8815] NA NA NA NA ...
## ..$ skyc3 : chr [1:8815] NA NA NA NA ...
## ..$ skyc4 : chr [1:8815] NA NA NA NA ...
## ..$ skyl1 : num [1:8815] 1600 1600 1500 1900 1700 1700 1600 1500 2000 1600 ...
## ..$ skyl2 : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl3 : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl4 : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ wxcodes : chr [1:8815] NA NA NA NA ...
## ..$ ice_accretion_1hr: logi [1:8815] NA NA NA NA NA NA ...
## ..$ ice_accretion_3hr: logi [1:8815] NA NA NA NA NA NA ...
## ..$ ice_accretion_6hr: logi [1:8815] NA NA NA NA NA NA ...
## ..$ peak_wind_gust : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_drct : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_time : POSIXct[1:8815], format: NA NA ...
## ..$ feel : num [1:8815] 17.9 17.9 17.9 17.2 19.5 ...
## ..$ metar : chr [1:8815] "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## ..- attr(*, "problems")=Classes 'tbl_df', 'tbl' and 'data.frame': 22 obs. of 5 variables:
## .. ..$ row : int [1:22] 1678 1680 1902 1903 1903 2919 2921 2922 2929 2934 ...
## .. ..$ col : chr [1:22] "ice_accretion_1hr" "ice_accretion_6hr" "ice_accretion_1hr" "ice_accretion_1hr" ...
## .. ..$ expected: chr [1:22] "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" ...
## .. ..$ actual : chr [1:22] "0.01" "0.01" "0.01" "0.01" ...
## .. ..$ file : chr [1:22] "'./RInputFiles/metar_kord_2016.txt'" "'./RInputFiles/metar_kord_2016.txt'" "'./RInputFiles/metar_kord_2016.txt'" "'./RInputFiles/metar_kord_2016.txt'" ...
## ..- attr(*, "spec")=
## .. .. cols(
## .. .. station = col_character(),
## .. .. valid = col_datetime(format = ""),
## .. .. tmpf = col_double(),
## .. .. dwpf = col_double(),
## .. .. relh = col_double(),
## .. .. drct = col_double(),
## .. .. sknt = col_double(),
## .. .. p01i = col_character(),
## .. .. alti = col_double(),
## .. .. mslp = col_double(),
## .. .. vsby = col_double(),
## .. .. gust = col_double(),
## .. .. skyc1 = col_character(),
## .. .. skyc2 = col_character(),
## .. .. skyc3 = col_character(),
## .. .. skyc4 = col_character(),
## .. .. skyl1 = col_double(),
## .. .. skyl2 = col_double(),
## .. .. skyl3 = col_double(),
## .. .. skyl4 = col_double(),
## .. .. wxcodes = col_character(),
## .. .. ice_accretion_1hr = col_logical(),
## .. .. ice_accretion_3hr = col_logical(),
## .. .. ice_accretion_6hr = col_logical(),
## .. .. peak_wind_gust = col_double(),
## .. .. peak_wind_drct = col_double(),
## .. .. peak_wind_time = col_datetime(format = ""),
## .. .. feel = col_double(),
## .. .. metar = col_character()
## .. .. )
## $ windMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 32 variables:
## ..$ station : chr [1:8815] "ORD" "ORD" "ORD" "ORD" ...
## ..$ valid : POSIXct[1:8815], format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## ..$ tmpf : num [1:8815] 27 27 27 27 27 ...
## ..$ dwpf : num [1:8815] 21.9 21.9 21 21 21 ...
## ..$ relh : num [1:8815] 81 81 78 78 78 ...
## ..$ drct : num [1:8815] 260 260 250 240 250 250 270 260 250 250 ...
## ..$ sknt : num [1:8815] 8 8 8 9 6 7 10 11 8 10 ...
## ..$ p01i : chr [1:8815] "0.00" "0.00" "0.00" "0.00" ...
## ..$ alti : num [1:8815] 30.2 30.2 30.2 30.2 30.2 ...
## ..$ mslp : num [1:8815] 1024 1024 1024 1024 1024 ...
## ..$ vsby : num [1:8815] 8 9 10 10 10 10 10 10 10 10 ...
## ..$ gust : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyc1 : chr [1:8815] "OVC" "OVC" "OVC" "OVC" ...
## ..$ skyc2 : chr [1:8815] NA NA NA NA ...
## ..$ skyc3 : chr [1:8815] NA NA NA NA ...
## ..$ skyc4 : chr [1:8815] NA NA NA NA ...
## ..$ skyl1 : num [1:8815] 1600 1600 1500 1900 1700 1700 1600 1500 2000 1600 ...
## ..$ skyl2 : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl3 : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl4 : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ wxcodes : chr [1:8815] NA NA NA NA ...
## ..$ ice_accretion_1hr: logi [1:8815] NA NA NA NA NA NA ...
## ..$ ice_accretion_3hr: logi [1:8815] NA NA NA NA NA NA ...
## ..$ ice_accretion_6hr: logi [1:8815] NA NA NA NA NA NA ...
## ..$ peak_wind_gust : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_drct : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_time : POSIXct[1:8815], format: NA NA ...
## ..$ feel : num [1:8815] 17.9 17.9 17.9 17.2 19.5 ...
## ..$ metar : chr [1:8815] "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## ..$ dirW : chr [1:8815] "260" "260" "250" "240" ...
## ..$ spdW : num [1:8815] 8 8 8 9 6 7 10 11 8 10 ...
## ..$ gustW : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## $ initMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 13 variables:
## ..$ METAR : chr [1:8815] "51Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "51Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "51Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061" "51Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## ..$ WindDir : chr [1:8815] "260" "260" "250" "240" ...
## ..$ WindSpeed : chr [1:8815] "08" "08" "08" "09" ...
## ..$ WindGust : chr [1:8815] NA NA NA NA ...
## ..$ Dummy : chr [1:8815] " " " " " " " " ...
## ..$ Visibility: chr [1:8815] "8SM" "9SM" "10SM" "10SM" ...
## ..$ TempC : chr [1:8815] "M03" "M03" "M03" "M03" ...
## ..$ DewC : chr [1:8815] "M06" "M06" "M06" "M06" ...
## ..$ Altimeter : chr [1:8815] "A3021" "A3021" "A3021" "A3022" ...
## ..$ SLP : chr [1:8815] "SLP239" "SLP240" "SLP239" "SLP242" ...
## ..$ FahrC : chr [1:8815] "T10281056" "T10281056" "T10281061" "T10281061" ...
## ..$ dtime : POSIXct[1:8815], format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## ..$ origMETAR : chr [1:8815] "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ convMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 15 variables:
## ..$ METAR : chr [1:8815] "51Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "51Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "51Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061" "51Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## ..$ WindDir : chr [1:8815] "260" "260" "250" "240" ...
## ..$ WindSpeed : int [1:8815] 8 8 8 9 6 7 10 11 8 10 ...
## ..$ WindGust : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ Dummy : chr [1:8815] " " " " " " " " ...
## ..$ Visibility: num [1:8815] 8 9 10 10 10 10 10 10 10 10 ...
## ..$ TempC : int [1:8815] -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 ...
## ..$ DewC : int [1:8815] -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 ...
## ..$ Altimeter : int [1:8815] 3021 3021 3021 3022 3021 3020 3021 3022 3022 3021 ...
## ..$ SLP : int [1:8815] 239 240 239 242 239 237 237 242 244 239 ...
## ..$ FahrC : chr [1:8815] "T10281056" "T10281056" "T10281061" "T10281061" ...
## ..$ dtime : POSIXct[1:8815], format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## ..$ origMETAR : chr [1:8815] "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## ..$ TempF : num [1:8815] 27 27 27 27 27 ...
## ..$ DewF : num [1:8815] 21.9 21.9 21 21 21 ...
## $ parseMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 16 variables:
## ..$ METAR : chr [1:8815] "51Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "51Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "51Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061" "51Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## ..$ WindDir : chr [1:8815] "260" "260" "250" "240" ...
## ..$ WindSpeed : int [1:8815] 8 8 8 9 6 7 10 11 8 10 ...
## ..$ WindGust : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ Dummy : chr [1:8815] " " " " " " " " ...
## ..$ Visibility: num [1:8815] 8 9 10 10 10 10 10 10 10 10 ...
## ..$ TempC : int [1:8815] -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 ...
## ..$ DewC : int [1:8815] -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 ...
## ..$ Altimeter : int [1:8815] 3021 3021 3021 3022 3021 3020 3021 3022 3022 3021 ...
## ..$ SLP : int [1:8815] 239 240 239 242 239 237 237 242 244 239 ...
## ..$ FahrC : chr [1:8815] "T10281056" "T10281056" "T10281061" "T10281061" ...
## ..$ dtime : POSIXct[1:8815], format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## ..$ origMETAR : chr [1:8815] "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## ..$ TempF : num [1:8815] 27 27 27 27 27 ...
## ..$ DewF : num [1:8815] 21.9 21.9 21 21 21 ...
## ..$ modSLP : num [1:8815] 1024 1024 1024 1024 1024 ...
## $ initClouds :List of 2
## ..$ tblClouds:Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 7 variables:
## .. ..$ isCLR : num [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ isVV : num [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ htVV : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ numFEW: int [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ numSCT: int [1:8815] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ numBKN: int [1:8815] 0 0 0 0 0 0 0 0 0 1 ...
## .. ..$ numOVC: int [1:8815] 1 1 1 1 1 1 1 1 1 0 ...
## ..$ mtxCloud : chr [1:8815, 1:14] "" "" "" "" ...
## $ processedClouds:List of 3
## ..$ lowCloud :Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 5 variables:
## .. ..$ lowVV : num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ lowOVC: num [1:8815] 1600 1600 1500 1900 1700 1700 1600 1500 2000 NA ...
## .. ..$ lowBKN: num [1:8815] NA NA NA NA NA NA NA NA NA 1600 ...
## .. ..$ lowSCT: num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ lowFEW: num [1:8815] NA NA NA NA NA NA NA NA NA NA ...
## ..$ minCeilingLevel: num [1:8815] 1600 1600 1500 1900 1700 1700 1600 1500 2000 1600 ...
## ..$ minCloudLevel : num [1:8815] 1600 1600 1500 1900 1700 1700 1600 1500 2000 1600 ...Example #24: Downloading the Data by Function
The Iowa State Mesonet website provides instructions for automating the download process using Python or R, including links to a GitHub repository with instructions for R download. The code can be adapted for use here.
Example code includes:
# Function to get ASOS data from Iowa State
getASOSData <- function(faaID, startDate, endDate, suffix,
dirDownload = "./RInputFiles/", getAgain=FALSE) {
# Create the file name and location for saving data
fileName <- paste0("metar_k", str_to_lower(faaID), "_", suffix, ".txt")
fileLoc <- paste0(dirDownload, fileName)
# Check whether the file already exists, stop if so and getAgain is FALSE, otherwise download the data
if (file.exists(fileLoc) & !getAgain) {
cat("\nFile already exists - ", fileLoc)
cat("\nStopping download routine\n")
return(FALSE)
} else {
# Get the year, day, and hour of the key dates
y1 <- lubridate::year(startDate)
m1 <- lubridate::month(startDate)
d1 <- lubridate::day(startDate)
y2 <- lubridate::year(endDate)
m2 <- lubridate::month(endDate)
d2 <- lubridate::day(endDate)
# Mimic the string shown below
# https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?station=LAS&data=all&year1=2015&month1=12&day1=31&year2=2017&month2=1&day2=2&tz=Etc%2FUTC&format=onlycomma&latlon=no&missing=M&trace=T&direct=no&report_type=2
baseURL <- "https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?" # base URL
useURL <- paste0(baseURL, "station=", faaID) # add the desired station
useURL <- paste0(useURL, "&data=all") # request all data fields
useURL <- paste0(useURL, "&year1=", y1, "&month1=", m1, "&day1=", d1) # Specify start ymd
useURL <- paste0(useURL, "&year2=", y2, "&month2=", m2, "&day2=", d2) # Specify end ymd
useURL <- paste0(useURL, "&tz=Etc%2FUTC&format=onlycomma&latlon=no") # Formatting
useURL <- paste0(useURL, "&missing=M&trace=T&direct=no&report_type=2") # Formatting
# Download the file
download.file(useURL, destfile=fileLoc, method="curl")
return(TRUE)
}
}And then cache the actual download step to minimize utilization of the Iowa State server (though the code is set to not download to an existing file anyway):
# Specify the FAA ID and Analysis Year
useFAAID <- "LAS"
analysisYear <- 2016
# Specify the start and end dates based on the analysis year
startDate <- ISOdate(analysisYear-1, 12, 31, hour=0)
endDate <- ISOdate(analysisYear+1, 1, 2, hour=0)
# Get the relevant data
getASOSData(faaID=useFAAID, startDate=startDate, endDate=endDate, suffix=analysisYear)## [1] TRUEAnd then explore the data to set the key parameters for a full run of the METAR process:
# Set key parameters for reading and interpreting METAR based on above inputs
fname <- paste0("./RInputFiles/metar_k", str_to_lower(useFAAID), "_", analysisYear, ".txt")
# Find the most common Zulu time (this will be the METAR)
zTimes <- readr::read_csv(fname) %>%
pull(metar) %>%
str_match(pattern="\\d{2}Z") %>%
as.vector() %>%
table() %>%
sort(decreasing=TRUE)## Parsed with column specification:
## cols(
## .default = col_character(),
## valid = col_datetime(format = ""),
## tmpf = col_double(),
## dwpf = col_double(),
## relh = col_double(),
## sknt = col_double(),
## alti = col_double(),
## vsby = col_double(),
## feel = col_double()
## )## See spec(...) for full column specifications.## Warning: 64 parsing failures.
## row col expected actual file
## 1585 relh a double M './RInputFiles/metar_klas_2016.txt'
## 1585 sknt a double M './RInputFiles/metar_klas_2016.txt'
## 1585 feel a double M './RInputFiles/metar_klas_2016.txt'
## 1869 relh a double M './RInputFiles/metar_klas_2016.txt'
## 1869 sknt a double M './RInputFiles/metar_klas_2016.txt'
## .... .... ........ ...... ...................................
## See problems(...) for more details.cat("\nThe most common Zulu time is", names(zTimes)[1], "\nFrequency is",
round(100*zTimes[1]/sum(zTimes), 1), "% (", zTimes[1], "of", sum(zTimes), ")"
)##
## The most common Zulu time is 56Z
## Frequency is 97.7 % ( 8818 of 9027 )# Use the most common Zulu time and start/end dates to set key parameters
timeZ <- names(zTimes)[1] # Zulu time that METAR is recorded at this station
expMin <- ISOdate(analysisYear-1, 12, 31, hour=0, min=as.integer(str_replace(timeZ, "Z", "")))
expDays <- as.integer(endDate - startDate) # Expected total days read
# Provide a descriptive name
locMET <- "Las Vegas, NV" # Description of city or location
shortMET <- "KLAS METAR (2016)" # Station code and timing
longMET <- "Las Vegas, NV Hourly METAR (2016)" # Description of city or location and timing
# Extraction format for METAR - paste the expected Zulu time at the front
valMet <- paste0(timeZ, ".*?(VRB|\\d{3})(\\d{2})(G\\d{2})?KT(.*?)(\\d{1,2}SM).*?\\s(M?\\d{2})/(M?\\d{2}).*?(A\\d{4}).*?RMK.*?(SLP\\d{3}).*?(T\\d{8})")
# Run the process for Lincoln, NE
klas2016METAR <- runAllMETAR(fname=fname, timeZ=timeZ, expMin=expMin, expDays=expDays,
locMET=locMET, shortMET=shortMET, longMET=longMET, valMet=valMet
)## Parsed with column specification:
## cols(
## .default = col_double(),
## station = col_character(),
## valid = col_datetime(format = ""),
## p01i = col_character(),
## skyc1 = col_character(),
## skyc2 = col_character(),
## skyc3 = col_character(),
## skyc4 = col_character(),
## wxcodes = col_character(),
## ice_accretion_1hr = col_logical(),
## ice_accretion_3hr = col_logical(),
## ice_accretion_6hr = col_logical(),
## peak_wind_time = col_datetime(format = ""),
## metar = col_character()
## )
## See spec(...) for full column specifications.## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 9027 obs. of 29 variables:
## $ station : chr "LAS" "LAS" "LAS" "LAS" ...
## $ valid : POSIXct, format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## $ tmpf : num 46.9 44.1 43 41 39.9 ...
## $ dwpf : num 18 17.1 16 16 17.1 ...
## $ relh : num 31.1 33.4 33.2 35.9 39.2 ...
## $ drct : num 100 10 10 340 210 230 230 0 0 210 ...
## $ sknt : num 3 5 5 5 3 3 3 0 0 3 ...
## $ p01i : chr "0.00" "0.00" "0.00" "0.00" ...
## $ alti : num 30.1 30.1 30.1 30.1 30.1 ...
## $ mslp : num 1020 1020 1021 1022 1022 ...
## $ vsby : num 10 10 10 10 10 10 10 10 10 10 ...
## $ gust : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr "CLR" "CLR" "CLR" "CLR" ...
## $ skyc2 : chr NA NA NA NA ...
## $ skyc3 : chr NA NA NA NA ...
## $ skyc4 : chr NA NA NA NA ...
## $ skyl1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyl2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyl3 : num NA NA NA NA NA NA NA NA NA NA ...
## $ skyl4 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr NA NA NA NA ...
## $ ice_accretion_1hr: logi NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi NA NA NA NA NA NA ...
## $ peak_wind_gust : num NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_drct : num NA NA NA NA NA NA NA NA NA NA ...
## $ peak_wind_time : POSIXct, format: NA NA ...
## $ feel : num 45.8 40.7 39.4 37.1 37.7 ...
## $ metar : chr "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
##
## *** OBSERVATIONS EXPECTED BUT NOT RECORDED ***
## [1] "2016-06-17 23:56:00 UTC" "2016-06-18 00:56:00 UTC"
## [3] "2016-07-13 14:56:00 UTC" "2016-07-13 15:56:00 UTC"
## [5] "2016-07-13 16:56:00 UTC" "2016-07-13 17:56:00 UTC"
## [7] "2016-08-05 07:56:00 UTC" "2016-08-12 17:56:00 UTC"
## [9] "2016-08-23 14:56:00 UTC" "2016-08-24 00:56:00 UTC"
## [11] "2016-08-24 01:56:00 UTC" "2016-11-21 00:56:00 UTC"
## [13] "2016-12-03 09:56:00 UTC" "2016-12-03 10:56:00 UTC"
##
## *** OBSERVATIONS RECORDED BUT NOT EXPECTED ***
## POSIXct of length 0
##
## *** Are the extracted records unique? ***
## [1] TRUE
##
##
## *** First 6 winds and parsing ***
## [,1] [,2] [,3] [,4]
## [1,] "10003KT" "100" "03" NA
## [2,] "01005KT" "010" "05" NA
## [3,] "01005KT" "010" "05" NA
## [4,] "34005KT" "340" "05" NA
## [5,] "21003KT" "210" "03" NA
## [6,] "23003KT" "230" "03" NA
##
## *** Table of WIND DIRECTION ***
##
## 000 010 020 030 040 050 060 070 080 090 100 110 120 130 140 150
## 1397 152 172 161 162 196 164 167 172 119 101 79 51 49 70 100
## 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310
## 186 297 436 629 594 543 512 348 200 135 118 118 112 81 51 59
## 320 330 340 350 360 VRB <NA>
## 76 145 126 113 104 504 19
##
## *** Table of WIND SPEED ***
##
## 00 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17
## 1397 1349 1247 963 727 500 474 375 386 301 244 192 161 121 88 58
## 18 19 20 21 22 23 24 25 26 27 28 29 31 <NA>
## 67 35 41 28 14 12 7 3 2 2 3 1 1 19
##
## *** Table of WIND GUST ***
##
## G14 G15 G16 G17 G18 G19 G20 G21 G22 G23 G24 G25 G26 G27 G28 G29
## 23 80 94 146 128 130 116 106 111 77 82 73 53 38 28 28
## G30 G31 G32 G33 G34 G35 G36 G37 G38 G39 G40 G41 G44 G45 <NA>
## 23 20 9 11 13 6 10 3 1 5 1 1 2 2 7398
##
## *** WIND DATA WAS NOT CAPTURED FROM: ***
## # A tibble: 19 x 1
## metar
## <chr>
## 1 KLAS 042156Z 10SM FEW200 BKN250 26/M09 A2988 RMK AO2 SLP100 T02561089
## 2 KLAS 161456Z 10SM FEW250 14/M06 A3010 RMK AO2 SLP180 T01441061 51021
## 3 KLAS 022256Z 10SM FEW090 SCT250 26/M01 A2997 RMK AO2 SLP134 T02611006
## 4 KLAS 032056Z 10SM FEW250 28/01 A3000 RMK AO2 SLP138 T02780006 57020
## 5 KLAS 132156Z 10SM FEW120 36/M03 A2989 RMK AO2 SLP095 T03611033
## 6 KLAS 151756Z 10SM FEW250 27/M01 A2970 RMK AO2 SLP030 T02671006 10272 20217 5~
## 7 KLAS 182156Z 10SM SCT130 SCT200 29/02 A2987 RMK AO2 SLP093 T02940022
## 8 KLAS 282056Z 10SM SCT120 BKN250 32/M03 A2979 RMK AO2 SLP061 T03171028 56016
## 9 KLAS 131856Z 10SM FEW120 32/01 A2993 RMK AO2 SLP108 T03170011
## 10 KLAS 221756Z 10SM FEW180 FEW250 38/M01 A2984 RMK AO2 SLP069 T03781006 10383 ~
## 11 KLAS 281856Z 10SM FEW095 SCT130 SCT200 40/04 A2995 RMK AO2 SLP106 T04000044
## 12 KLAS 012256Z 10SM FEW100 FEW160 SCT250 35/13 A2981 RMK AO2 SLP068 TCU SW AND~
## 13 KLAS 241656Z 9SM CLR 38/02 A2986 RMK AO2 SLP077 FU ALQDS VIS LWR S T03780022
## 14 KLAS 011956Z 10SM SCT120 SCT250 37/16 A2990 RMK AO2 SLP095 TCU DSNT N AND W ~
## 15 KLAS 061856Z 10SM FEW100 FEW140 36/09 A2982 RMK AO2 SLP066 T03610094
## 16 KLAS 112156Z 10SM FEW110 FEW200 37/00 A2992 RMK AO2 SLP100 CB DSNT SE ACC DS~
## 17 KLAS 222256Z 10SM SCT120 SCT200 36/04 A2978 RMK AO2 SLP055 CB DSNT SE ACC DS~
## 18 KLAS 092156Z 10SM FEW120 37/M01 A2993 RMK AO2 SLP106 T03721006
## 19 KLAS 130456Z 10SM CLR 29/01 A2968 RMK AO2 SLP023 T02890006
## Warning: Removed 19 rows containing non-finite values (stat_count).
## Warning: Removed 1 rows containing missing values (geom_point).##
## *** Tentative Summary of Element Parsing ***
## .
## FALSE TRUE
## 35 8783
##
## *** Data Not Matched ***
## [1] "KLAS 042156Z 10SM FEW200 BKN250 26/M09 A2988 RMK AO2 SLP100 T02561089"
## [2] "KLAS 161456Z 10SM FEW250 14/M06 A3010 RMK AO2 SLP180 T01441061 51021"
## [3] "KLAS 022256Z 10SM FEW090 SCT250 26/M01 A2997 RMK AO2 SLP134 T02611006"
## [4] "KLAS 032056Z 10SM FEW250 28/01 A3000 RMK AO2 SLP138 T02780006 57020"
## [5] "KLAS 132156Z 10SM FEW120 36/M03 A2989 RMK AO2 SLP095 T03611033"
## [6] "KLAS 151756Z 10SM FEW250 27/M01 A2970 RMK AO2 SLP030 T02671006 10272 20217 51002"
## [7] "KLAS 182156Z 10SM SCT130 SCT200 29/02 A2987 RMK AO2 SLP093 T02940022"
## [8] "KLAS 282056Z 10SM SCT120 BKN250 32/M03 A2979 RMK AO2 SLP061 T03171028 56016"
## [9] "KLAS 131856Z 10SM FEW120 32/01 A2993 RMK AO2 SLP108 T03170011"
## [10] "KLAS 221756Z 10SM FEW180 FEW250 38/M01 A2984 RMK AO2 SLP069 T03781006 10383 20306 50003"
## [11] "KLAS 281856Z 10SM FEW095 SCT130 SCT200 40/04 A2995 RMK AO2 SLP106 T04000044"
## [12] "KLAS 012256Z 10SM FEW100 FEW160 SCT250 35/13 A2981 RMK AO2 SLP068 TCU SW AND DSNT S T03500133"
## [13] "KLAS 241656Z 9SM CLR 38/02 A2986 RMK AO2 SLP077 FU ALQDS VIS LWR S T03780022"
## [14] "KLAS 011956Z 10SM SCT120 SCT250 37/16 A2990 RMK AO2 SLP095 TCU DSNT N AND W AND NW ACC DSNT S-SW T03670156"
## [15] "KLAS 061856Z 10SM FEW100 FEW140 36/09 A2982 RMK AO2 SLP066 T03610094"
## [16] "KLAS 112156Z 10SM FEW110 FEW200 37/00 A2992 RMK AO2 SLP100 CB DSNT SE ACC DSNT E T03720000"
## [17] "KLAS 222256Z 10SM SCT120 SCT200 36/04 A2978 RMK AO2 SLP055 CB DSNT SE ACC DSNT ALQDS T03610044"
## [18] "KLAS 231556Z 00000KT 10SM FEW150 SCT180 26/14 A3005 RMK SLPNO T02570144"
## [19] "KLAS 231656Z 10003KT 10SM FEW180 28/14 A3006 RMK SLPNO T02790143"
## [20] "KLAS 231756Z 13004KT 10SM FEW100 FEW180 27/14 A3006 RMK SLPNO T02730139"
## [21] "KLAS 231856Z 00000KT 10SM FEW100 30/13 A3004 RMK SLPNO T03010127"
## [22] "KLAS 231956Z 00000KT 10SM FEW100 31/13 A3002 RMK SLPNO T03130131"
## [23] "KLAS 232056Z 03004KT 10SM FEW100 31/13 A2999 RMK SLPNO CB DSNT NE T03080131"
## [24] "KLAS 232156Z 10005KT 10SM FEW100 32/13 A2996 RMK SLPNO TCU DSNT N AND NW T03160132"
## [25] "KLAS 232256Z 08005KT 10SM FEW100 32/13 A2994 RMK SLPNO TCU DSNT W-NW T03210134"
## [26] "KLAS 232356Z 05006KT 10SM FEW100 FEW250 32/14 A2992 RMK SLPNO ACC W T03240136"
## [27] "KLAS 240256Z 01007KT 10SM FEW100 FEW250 32/11 A2994 RMK AO2 SLPNO CB DSNT N T03220106 53004 $"
## [28] "KLAS 240356Z 05011KT 10SM FEW100 FEW250 32/11 A2997 RMK AO2 SLPNO T03170106 $"
## [29] "KLAS 240456Z 02007KT 10SM SCT100 SCT250 31/12 A2998 RMK AO2 SLPNO T03060122 $"
## [30] "KLAS 240556Z 34007KT 10SM SCT120 BKN160 BKN250 31/11 A3000 RMK AO2 SLPNO OCNL LTGIC DSNT NW T030600111 10339 20300 51018 $"
## [31] "KLAS 240656Z VRB03KT 10SM -TSRA FEW030 SCT095CB BKN150 27/13 A3001 RMK AO2 RAB40 TSB38 SLPNO OCNL LTGICCG W-N TS W-N MOV S P0003 T02720133 $"
## [32] "KLAS 240756Z 26010KT 10SM TS FEW050 FEW110CB BKN150 27/13 A2999 RMK AO2 RAE29 SLPNO OCNL LTGICCG S-SW TS S-SW MOV S P0000 T02720133 403390267 $"
## [33] "KLAS 092156Z 10SM FEW120 37/M01 A2993 RMK AO2 SLP106 T03721006"
## [34] "KLAS 130456Z 10SM CLR 29/01 A2968 RMK AO2 SLP023 T02890006"
## [35] "KLAS 071456Z 02010KT 10SM BKN250 07/M13 A3011"
##
## *** Parsing matrix summary ***
## [1] 8818 11
## [,1] [,2] [,3]
## [1,] "56Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "100" "03"
## [2,] "56Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "010" "05"
## [3,] "56Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089" "010" "05"
## [4,] "56Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" "340" "05"
## [5,] "56Z 21003KT 10SM CLR 04/M08 A3015 RMK AO2 SLP216 T00441083" "210" "03"
## [6,] "56Z 23003KT 10SM CLR 03/M08 A3016 RMK AO2 SLP219 T00331083" "230" "03"
## [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
## [1,] NA " " "10SM" "08" "M08" "A3009" "SLP196" "T00831078"
## [2,] NA " " "10SM" "07" "M08" "A3011" "SLP204" "T00671083"
## [3,] NA " " "10SM" "06" "M09" "A3013" "SLP210" "T00611089"
## [4,] NA " " "10SM" "05" "M09" "A3014" "SLP215" "T00501089"
## [5,] NA " " "10SM" "04" "M08" "A3015" "SLP216" "T00441083"
## [6,] NA " " "10SM" "03" "M08" "A3016" "SLP219" "T00331083"
##
## *** Summary of the parsed data ***
## Observations: 8,818
## Variables: 13
## $ METAR <chr> "56Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831...
## $ WindDir <chr> "100", "010", "010", "340", "210", "230", "230", "000", ...
## $ WindSpeed <chr> "03", "05", "05", "05", "03", "03", "03", "00", "00", "0...
## $ WindGust <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ Dummy <chr> " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "...
## $ Visibility <chr> "10SM", "10SM", "10SM", "10SM", "10SM", "10SM", "10SM", ...
## $ TempC <chr> "08", "07", "06", "05", "04", "03", "02", "02", "01", "0...
## $ DewC <chr> "M08", "M08", "M09", "M09", "M08", "M08", "M09", "M09", ...
## $ Altimeter <chr> "A3009", "A3011", "A3013", "A3014", "A3015", "A3016", "A...
## $ SLP <chr> "SLP196", "SLP204", "SLP210", "SLP215", "SLP216", "SLP21...
## $ FahrC <chr> "T00831078", "T00671083", "T00611089", "T00501089", "T00...
## $ dtime <dttm> 2015-12-31 00:56:00, 2015-12-31 01:56:00, 2015-12-31 02...
## $ origMETAR <chr> "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP1...## Warning: NAs introduced by coercion
##
## *** Parsed data structure, head, tail, and random sample ***
## Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 15 variables:
## $ METAR : chr "56Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "56Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "56Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089" "56Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## $ WindDir : chr "100" "010" "010" "340" ...
## $ WindSpeed : int 3 5 5 5 3 3 3 0 0 3 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 10 10 10 10 10 10 10 10 10 10 ...
## $ TempC : int 8 7 6 5 4 3 2 2 1 1 ...
## $ DewC : int -8 -8 -9 -9 -8 -8 -9 -9 -8 -8 ...
## $ Altimeter : int 3009 3011 3013 3014 3015 3016 3017 3017 3018 3019 ...
## $ SLP : int 196 204 210 215 216 219 221 222 226 229 ...
## $ FahrC : chr "T00831078" "T00671083" "T00611089" "T00501089" ...
## $ dtime : POSIXct, format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## $ origMETAR : chr "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## $ TempF : num 46.9 44.1 43 41 39.9 ...
## $ DewF : num 18 17.1 16 16 17.1 ...
## # A tibble: 6 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 56Z ~ 100 3 NA " " 10 8 -8 3009 196
## 2 56Z ~ 010 5 NA " " 10 7 -8 3011 204
## 3 56Z ~ 010 5 NA " " 10 6 -9 3013 210
## 4 56Z ~ 340 5 NA " " 10 5 -9 3014 215
## 5 56Z ~ 210 3 NA " " 10 4 -8 3015 216
## 6 56Z ~ 230 3 NA " " 10 3 -8 3016 219
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
## # A tibble: 6 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 56Z ~ 000 0 NA " " 10 12 4 2979 84
## 2 56Z ~ 170 4 NA " " 10 13 3 2975 71
## 3 56Z ~ 150 6 NA " " 10 14 3 2973 62
## 4 56Z ~ VRB 5 NA " " 10 15 2 2970 50
## 5 56Z ~ 180 10 NA " " 10 15 2 2970 51
## 6 56Z ~ 180 11 NA " " 10 13 3 2971 56
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
## # A tibble: 20 x 15
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC DewC Altimeter SLP
## <chr> <chr> <int> <dbl> <chr> <dbl> <int> <int> <int> <int>
## 1 56Z ~ 220 11 NA " " 10 41 -3 2971 25
## 2 56Z ~ 000 0 NA " " 10 19 2 2975 58
## 3 56Z ~ 290 4 NA " " 10 12 -8 2988 112
## 4 56Z ~ 220 13 NA " " 10 19 -4 2958 997
## 5 56Z ~ 010 3 NA " " 10 13 2 3011 192
## 6 56Z ~ 200 5 NA " " 10 31 -4 2981 61
## 7 56Z ~ 000 0 NA " " 10 22 14 3002 139
## 8 56Z ~ 000 0 NA " " 10 10 -9 3032 273
## 9 56Z ~ 260 4 NA " " 10 28 1 2997 117
## 10 56Z ~ 220 3 NA " " 10 28 -4 2993 111
## 11 56Z ~ 120 4 NA " " 10 28 -4 2996 126
## 12 <NA> <NA> NA NA <NA> NA NA NA NA NA
## 13 56Z ~ 000 0 NA " " 9 8 4 3006 176
## 14 56Z ~ 230 10 NA " " 10 30 -1 2987 91
## 15 56Z ~ 000 0 NA " " 10 10 4 2999 149
## 16 56Z ~ 210 5 NA " " 10 32 -7 2978 51
## 17 56Z ~ 230 5 NA " " 10 16 6 2996 129
## 18 56Z ~ 200 4 NA " " 10 5 0 2982 97
## 19 56Z ~ 180 3 NA " " 6 8 7 2996 145
## 20 56Z ~ 200 3 NA " " 10 28 -1 2992 98
## # ... with 5 more variables: FahrC <chr>, dtime <dttm>, origMETAR <chr>,
## # TempF <dbl>, DewF <dbl>
##
## *** Number of NA values ***
## METAR WindDir WindSpeed WindGust Dummy Visibility TempC
## 35 35 35 8818 35 35 35
## DewC Altimeter SLP FahrC dtime origMETAR TempF
## 35 35 35 35 0 0 35
## DewF
## 35
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(diff(sort(x))): no non-missing arguments to min; returning Inf## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## # A tibble: 9 x 2
## Visibility n
## <dbl> <int>
## 1 3 4
## 2 4 8
## 3 5 11
## 4 6 13
## 5 7 16
## 6 8 26
## 7 9 42
## 8 10 8663
## 9 NA 35
## WindGust n
## 1 14 20
## 2 15 77
## 3 16 91
## 4 17 146
## 5 18 126
## 6 19 126
## 7 20 116
## 8 21 106
## 9 22 111
## 10 23 77
## 11 24 82
## 12 25 73
## 13 26 53
## 14 27 38
## 15 28 28
## 16 29 28
## 17 30 23
## 18 31 20
## 19 32 9
## 20 33 11
## 21 34 13
## 22 35 6
## 23 36 10
## 24 37 3
## 25 38 1
## 26 39 5
## 27 40 1
## 28 41 1
## 29 44 2
## 30 45 2
## 31 NA 7413## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).
##
## *** Correlations use 8783 complete cases (99.6% of 8818 total) ***
## TempC TempF DewC DewF Altimeter modSLP WindSpeed Visibility
## TempC 1.00 1.00 0.22 0.22 -0.51 -0.62 0.22 0.01
## TempF 1.00 1.00 0.22 0.22 -0.51 -0.62 0.22 0.01
## DewC 0.22 0.22 1.00 1.00 -0.24 -0.27 -0.04 -0.13
## DewF 0.22 0.22 1.00 1.00 -0.24 -0.27 -0.04 -0.13
## Altimeter -0.51 -0.51 -0.24 -0.24 1.00 0.99 -0.38 0.06
## modSLP -0.62 -0.62 -0.27 -0.27 0.99 1.00 -0.38 0.06
## WindSpeed 0.22 0.22 -0.04 -0.04 -0.38 -0.38 1.00 -0.02
## Visibility 0.01 0.01 -0.13 -0.13 0.06 0.06 -0.02 1.00
## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
##
## *** Regression call is: modSLP ~ Altimeter ***
##
## Call:
## lm(formula = formula(myChar), data = met)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1381 -0.7848 -0.0607 0.6891 3.6362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.163e+02 1.730e+00 -67.24 <2e-16 ***
## Altimeter 3.769e-01 5.776e-04 652.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9753 on 8781 degrees of freedom
## (35 observations deleted due to missingness)
## Multiple R-squared: 0.9798, Adjusted R-squared: 0.9798
## F-statistic: 4.258e+05 on 1 and 8781 DF, p-value: < 2.2e-16## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
##
## *** Regression call is: modSLP ~ Altimeter + TempF ***
##
## Call:
## lm(formula = formula(myChar), data = met)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.18161 -0.33113 0.02395 0.33395 1.08509
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.537e+01 8.693e-01 -29.18 <2e-16 ***
## Altimeter 3.478e-01 2.868e-04 1212.89 <2e-16 ***
## TempF -5.581e-02 2.813e-04 -198.42 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4165 on 8780 degrees of freedom
## (35 observations deleted due to missingness)
## Multiple R-squared: 0.9963, Adjusted R-squared: 0.9963
## F-statistic: 1.187e+06 on 2 and 8780 DF, p-value: < 2.2e-16## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
##
## No Records with a cloud type of vertical visibility (VV)
##
## *** Counts by number of layers of each cloud type ***
## isCLR isVV numFEW numSCT numBKN numOVC n
## 1 0 0 0 0 0 1 144
## 2 0 0 0 0 1 0 635
## 3 0 0 0 0 1 1 107
## 4 0 0 0 0 2 0 139
## 5 0 0 0 0 2 1 39
## 6 0 0 0 0 3 0 13
## 7 0 0 0 0 4 0 1
## 8 0 0 0 1 0 0 590
## 9 0 0 0 1 0 1 54
## 10 0 0 0 1 1 0 242
## 11 0 0 0 1 1 1 51
## 12 0 0 0 1 2 0 88
## 13 0 0 0 1 2 1 1
## 14 0 0 0 1 3 0 1
## 15 0 0 0 2 0 0 134
## 16 0 0 0 2 0 1 7
## 17 0 0 0 2 1 0 37
## 18 0 0 0 2 2 0 4
## 19 0 0 0 3 0 0 4
## 20 0 0 0 3 1 0 2
## 21 0 0 1 0 0 0 1933
## 22 0 0 1 0 0 1 99
## 23 0 0 1 0 1 0 454
## 24 0 0 1 0 1 1 92
## 25 0 0 1 0 2 0 140
## 26 0 0 1 0 2 1 2
## 27 0 0 1 0 3 0 2
## 28 0 0 1 1 0 0 330
## 29 0 0 1 1 0 1 34
## 30 0 0 1 1 1 0 212
## 31 0 0 1 1 1 1 17
## 32 0 0 1 1 2 0 26
## 33 0 0 1 2 0 0 53
## 34 0 0 1 2 0 1 1
## 35 0 0 1 2 1 0 9
## 36 0 0 1 3 0 0 1
## 37 0 0 2 0 0 0 533
## 38 0 0 2 0 0 1 18
## 39 0 0 2 0 1 0 64
## 40 0 0 2 0 1 1 2
## 41 0 0 2 0 2 0 3
## 42 0 0 2 1 0 0 75
## 43 0 0 2 1 0 1 1
## 44 0 0 2 1 1 0 5
## 45 0 0 2 2 0 0 3
## 46 0 0 3 0 0 0 4
## 47 0 0 3 0 0 1 1
## 48 0 0 3 0 1 0 1
## 49 0 0 3 1 0 0 2
## 50 0 0 3 1 1 0 1
## 51 1 0 0 0 0 0 2407
##
## *** METAR records where no clouds were extracted ***
## character(0)##
## *** Dimensions for the cloud matrix ***
## [1] 8818 12
## List of 2
## $ tblClouds:Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 7 variables:
## ..$ isCLR : num [1:8818] 1 1 1 1 1 1 1 1 1 1 ...
## ..$ isVV : num [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ htVV : logi [1:8818] NA NA NA NA NA NA ...
## ..$ numFEW: int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numSCT: int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numBKN: int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numOVC: int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## $ mtxCloud : chr [1:8818, 1:12] "" "" "" "" ...## Warning in min(isKey): no non-missing arguments to min; returning Inf## Warning in stri_replace_first_regex(string, pattern,
## fix_replacement(replacement), : NAs introduced by coercion to integer range
##
## *** Lowest clouds by type tibble ***
## # A tibble: 8,818 x 5
## lowVV lowOVC lowBKN lowSCT lowFEW
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## 7 NA NA NA NA NA
## 8 NA NA NA NA NA
## 9 NA NA NA NA NA
## 10 NA NA NA NA NA
## # ... with 8,808 more rows
## List of 3
## $ lowCloud :Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 5 variables:
## ..$ lowVV : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowOVC: num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowBKN: num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowSCT: num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowFEW: num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## $ minCeilingLevel: num [1:8818] 1e+06 1e+06 1e+06 1e+06 1e+06 ...
## $ minCloudLevel : num [1:8818] 1e+06 1e+06 1e+06 1e+06 1e+06 ...
## Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 30 variables:
## $ METAR : chr "56Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "56Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "56Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089" "56Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## $ WindDir : chr "100" "010" "010" "340" ...
## $ WindSpeed : int 3 5 5 5 3 3 3 0 0 3 ...
## $ WindGust : num NA NA NA NA NA NA NA NA NA NA ...
## $ Dummy : chr " " " " " " " " ...
## $ Visibility: num 10 10 10 10 10 10 10 10 10 10 ...
## $ TempC : int 8 7 6 5 4 3 2 2 1 1 ...
## $ DewC : int -8 -8 -9 -9 -8 -8 -9 -9 -8 -8 ...
## $ Altimeter : int 3009 3011 3013 3014 3015 3016 3017 3017 3018 3019 ...
## $ SLP : int 196 204 210 215 216 219 221 222 226 229 ...
## $ FahrC : chr "T00831078" "T00671083" "T00611089" "T00501089" ...
## $ dtime : POSIXct, format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## $ origMETAR : chr "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## $ TempF : num 46.9 44.1 43 41 39.9 ...
## $ DewF : num 18 17.1 16 16 17.1 ...
## $ modSLP : num 1020 1020 1021 1022 1022 ...
## $ isCLR : num 1 1 1 1 1 1 1 1 1 1 ...
## $ isVV : num 0 0 0 0 0 0 0 0 0 0 ...
## $ htVV : logi NA NA NA NA NA NA ...
## $ numFEW : int 0 0 0 0 0 0 0 0 0 0 ...
## $ numSCT : int 0 0 0 0 0 0 0 0 0 0 ...
## $ numBKN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ numOVC : int 0 0 0 0 0 0 0 0 0 0 ...
## $ lowVV : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowOVC : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowBKN : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowSCT : num NA NA NA NA NA NA NA NA NA NA ...
## $ lowFEW : num NA NA NA NA NA NA NA NA NA NA ...
## $ wType : Factor w/ 7 levels "VV","OVC","BKN",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ month : Factor w/ 12 levels "Jan","Feb","Mar",..: 12 12 12 12 12 12 12 12 12 12 ...
str(klas2016METAR)## List of 8
## $ fullMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 30 variables:
## ..$ METAR : chr [1:8818] "56Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "56Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "56Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089" "56Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## ..$ WindDir : chr [1:8818] "100" "010" "010" "340" ...
## ..$ WindSpeed : int [1:8818] 3 5 5 5 3 3 3 0 0 3 ...
## ..$ WindGust : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ Dummy : chr [1:8818] " " " " " " " " ...
## ..$ Visibility: num [1:8818] 10 10 10 10 10 10 10 10 10 10 ...
## ..$ TempC : int [1:8818] 8 7 6 5 4 3 2 2 1 1 ...
## ..$ DewC : int [1:8818] -8 -8 -9 -9 -8 -8 -9 -9 -8 -8 ...
## ..$ Altimeter : int [1:8818] 3009 3011 3013 3014 3015 3016 3017 3017 3018 3019 ...
## ..$ SLP : int [1:8818] 196 204 210 215 216 219 221 222 226 229 ...
## ..$ FahrC : chr [1:8818] "T00831078" "T00671083" "T00611089" "T00501089" ...
## ..$ dtime : POSIXct[1:8818], format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## ..$ origMETAR : chr [1:8818] "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## ..$ TempF : num [1:8818] 46.9 44.1 43 41 39.9 ...
## ..$ DewF : num [1:8818] 18 17.1 16 16 17.1 ...
## ..$ modSLP : num [1:8818] 1020 1020 1021 1022 1022 ...
## ..$ isCLR : num [1:8818] 1 1 1 1 1 1 1 1 1 1 ...
## ..$ isVV : num [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ htVV : logi [1:8818] NA NA NA NA NA NA ...
## ..$ numFEW : int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numSCT : int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numBKN : int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ numOVC : int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ lowVV : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowOVC : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowBKN : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowSCT : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ lowFEW : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ wType : Factor w/ 7 levels "VV","OVC","BKN",..: 6 6 6 6 6 6 6 6 6 6 ...
## ..$ month : Factor w/ 12 levels "Jan","Feb","Mar",..: 12 12 12 12 12 12 12 12 12 12 ...
## $ funcMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 29 variables:
## ..$ station : chr [1:8818] "LAS" "LAS" "LAS" "LAS" ...
## ..$ valid : POSIXct[1:8818], format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## ..$ tmpf : num [1:8818] 46.9 44.1 43 41 39.9 ...
## ..$ dwpf : num [1:8818] 18 17.1 16 16 17.1 ...
## ..$ relh : num [1:8818] 31.1 33.4 33.2 35.9 39.2 ...
## ..$ drct : num [1:8818] 100 10 10 340 210 230 230 0 0 210 ...
## ..$ sknt : num [1:8818] 3 5 5 5 3 3 3 0 0 3 ...
## ..$ p01i : chr [1:8818] "0.00" "0.00" "0.00" "0.00" ...
## ..$ alti : num [1:8818] 30.1 30.1 30.1 30.1 30.1 ...
## ..$ mslp : num [1:8818] 1020 1020 1021 1022 1022 ...
## ..$ vsby : num [1:8818] 10 10 10 10 10 10 10 10 10 10 ...
## ..$ gust : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyc1 : chr [1:8818] "CLR" "CLR" "CLR" "CLR" ...
## ..$ skyc2 : chr [1:8818] NA NA NA NA ...
## ..$ skyc3 : chr [1:8818] NA NA NA NA ...
## ..$ skyc4 : chr [1:8818] NA NA NA NA ...
## ..$ skyl1 : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl2 : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl3 : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl4 : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ wxcodes : chr [1:8818] NA NA NA NA ...
## ..$ ice_accretion_1hr: logi [1:8818] NA NA NA NA NA NA ...
## ..$ ice_accretion_3hr: logi [1:8818] NA NA NA NA NA NA ...
## ..$ ice_accretion_6hr: logi [1:8818] NA NA NA NA NA NA ...
## ..$ peak_wind_gust : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_drct : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_time : POSIXct[1:8818], format: NA NA ...
## ..$ feel : num [1:8818] 45.8 40.7 39.4 37.1 37.7 ...
## ..$ metar : chr [1:8818] "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## ..- attr(*, "spec")=
## .. .. cols(
## .. .. station = col_character(),
## .. .. valid = col_datetime(format = ""),
## .. .. tmpf = col_double(),
## .. .. dwpf = col_double(),
## .. .. relh = col_double(),
## .. .. drct = col_double(),
## .. .. sknt = col_double(),
## .. .. p01i = col_character(),
## .. .. alti = col_double(),
## .. .. mslp = col_double(),
## .. .. vsby = col_double(),
## .. .. gust = col_double(),
## .. .. skyc1 = col_character(),
## .. .. skyc2 = col_character(),
## .. .. skyc3 = col_character(),
## .. .. skyc4 = col_character(),
## .. .. skyl1 = col_double(),
## .. .. skyl2 = col_double(),
## .. .. skyl3 = col_double(),
## .. .. skyl4 = col_double(),
## .. .. wxcodes = col_character(),
## .. .. ice_accretion_1hr = col_logical(),
## .. .. ice_accretion_3hr = col_logical(),
## .. .. ice_accretion_6hr = col_logical(),
## .. .. peak_wind_gust = col_double(),
## .. .. peak_wind_drct = col_double(),
## .. .. peak_wind_time = col_datetime(format = ""),
## .. .. feel = col_double(),
## .. .. metar = col_character()
## .. .. )
## $ windMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 32 variables:
## ..$ station : chr [1:8818] "LAS" "LAS" "LAS" "LAS" ...
## ..$ valid : POSIXct[1:8818], format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## ..$ tmpf : num [1:8818] 46.9 44.1 43 41 39.9 ...
## ..$ dwpf : num [1:8818] 18 17.1 16 16 17.1 ...
## ..$ relh : num [1:8818] 31.1 33.4 33.2 35.9 39.2 ...
## ..$ drct : num [1:8818] 100 10 10 340 210 230 230 0 0 210 ...
## ..$ sknt : num [1:8818] 3 5 5 5 3 3 3 0 0 3 ...
## ..$ p01i : chr [1:8818] "0.00" "0.00" "0.00" "0.00" ...
## ..$ alti : num [1:8818] 30.1 30.1 30.1 30.1 30.1 ...
## ..$ mslp : num [1:8818] 1020 1020 1021 1022 1022 ...
## ..$ vsby : num [1:8818] 10 10 10 10 10 10 10 10 10 10 ...
## ..$ gust : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyc1 : chr [1:8818] "CLR" "CLR" "CLR" "CLR" ...
## ..$ skyc2 : chr [1:8818] NA NA NA NA ...
## ..$ skyc3 : chr [1:8818] NA NA NA NA ...
## ..$ skyc4 : chr [1:8818] NA NA NA NA ...
## ..$ skyl1 : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl2 : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl3 : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ skyl4 : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ wxcodes : chr [1:8818] NA NA NA NA ...
## ..$ ice_accretion_1hr: logi [1:8818] NA NA NA NA NA NA ...
## ..$ ice_accretion_3hr: logi [1:8818] NA NA NA NA NA NA ...
## ..$ ice_accretion_6hr: logi [1:8818] NA NA NA NA NA NA ...
## ..$ peak_wind_gust : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_drct : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ peak_wind_time : POSIXct[1:8818], format: NA NA ...
## ..$ feel : num [1:8818] 45.8 40.7 39.4 37.1 37.7 ...
## ..$ metar : chr [1:8818] "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## ..$ dirW : chr [1:8818] "100" "010" "010" "340" ...
## ..$ spdW : num [1:8818] 3 5 5 5 3 3 3 0 0 3 ...
## ..$ gustW : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## $ initMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 13 variables:
## ..$ METAR : chr [1:8818] "56Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "56Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "56Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089" "56Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## ..$ WindDir : chr [1:8818] "100" "010" "010" "340" ...
## ..$ WindSpeed : chr [1:8818] "03" "05" "05" "05" ...
## ..$ WindGust : chr [1:8818] NA NA NA NA ...
## ..$ Dummy : chr [1:8818] " " " " " " " " ...
## ..$ Visibility: chr [1:8818] "10SM" "10SM" "10SM" "10SM" ...
## ..$ TempC : chr [1:8818] "08" "07" "06" "05" ...
## ..$ DewC : chr [1:8818] "M08" "M08" "M09" "M09" ...
## ..$ Altimeter : chr [1:8818] "A3009" "A3011" "A3013" "A3014" ...
## ..$ SLP : chr [1:8818] "SLP196" "SLP204" "SLP210" "SLP215" ...
## ..$ FahrC : chr [1:8818] "T00831078" "T00671083" "T00611089" "T00501089" ...
## ..$ dtime : POSIXct[1:8818], format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## ..$ origMETAR : chr [1:8818] "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## $ convMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 15 variables:
## ..$ METAR : chr [1:8818] "56Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "56Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "56Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089" "56Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## ..$ WindDir : chr [1:8818] "100" "010" "010" "340" ...
## ..$ WindSpeed : int [1:8818] 3 5 5 5 3 3 3 0 0 3 ...
## ..$ WindGust : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ Dummy : chr [1:8818] " " " " " " " " ...
## ..$ Visibility: num [1:8818] 10 10 10 10 10 10 10 10 10 10 ...
## ..$ TempC : int [1:8818] 8 7 6 5 4 3 2 2 1 1 ...
## ..$ DewC : int [1:8818] -8 -8 -9 -9 -8 -8 -9 -9 -8 -8 ...
## ..$ Altimeter : int [1:8818] 3009 3011 3013 3014 3015 3016 3017 3017 3018 3019 ...
## ..$ SLP : int [1:8818] 196 204 210 215 216 219 221 222 226 229 ...
## ..$ FahrC : chr [1:8818] "T00831078" "T00671083" "T00611089" "T00501089" ...
## ..$ dtime : POSIXct[1:8818], format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## ..$ origMETAR : chr [1:8818] "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## ..$ TempF : num [1:8818] 46.9 44.1 43 41 39.9 ...
## ..$ DewF : num [1:8818] 18 17.1 16 16 17.1 ...
## $ parseMETAR :Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 16 variables:
## ..$ METAR : chr [1:8818] "56Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "56Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "56Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089" "56Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## ..$ WindDir : chr [1:8818] "100" "010" "010" "340" ...
## ..$ WindSpeed : int [1:8818] 3 5 5 5 3 3 3 0 0 3 ...
## ..$ WindGust : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ Dummy : chr [1:8818] " " " " " " " " ...
## ..$ Visibility: num [1:8818] 10 10 10 10 10 10 10 10 10 10 ...
## ..$ TempC : int [1:8818] 8 7 6 5 4 3 2 2 1 1 ...
## ..$ DewC : int [1:8818] -8 -8 -9 -9 -8 -8 -9 -9 -8 -8 ...
## ..$ Altimeter : int [1:8818] 3009 3011 3013 3014 3015 3016 3017 3017 3018 3019 ...
## ..$ SLP : int [1:8818] 196 204 210 215 216 219 221 222 226 229 ...
## ..$ FahrC : chr [1:8818] "T00831078" "T00671083" "T00611089" "T00501089" ...
## ..$ dtime : POSIXct[1:8818], format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## ..$ origMETAR : chr [1:8818] "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## ..$ TempF : num [1:8818] 46.9 44.1 43 41 39.9 ...
## ..$ DewF : num [1:8818] 18 17.1 16 16 17.1 ...
## ..$ modSLP : num [1:8818] 1020 1020 1021 1022 1022 ...
## $ initClouds :List of 2
## ..$ tblClouds:Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 7 variables:
## .. ..$ isCLR : num [1:8818] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ isVV : num [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ htVV : logi [1:8818] NA NA NA NA NA NA ...
## .. ..$ numFEW: int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ numSCT: int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ numBKN: int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..$ numOVC: int [1:8818] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ mtxCloud : chr [1:8818, 1:12] "" "" "" "" ...
## $ processedClouds:List of 3
## ..$ lowCloud :Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 5 variables:
## .. ..$ lowVV : num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ lowOVC: num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ lowBKN: num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ lowSCT: num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## .. ..$ lowFEW: num [1:8818] NA NA NA NA NA NA NA NA NA NA ...
## ..$ minCeilingLevel: num [1:8818] 1e+06 1e+06 1e+06 1e+06 1e+06 ...
## ..$ minCloudLevel : num [1:8818] 1e+06 1e+06 1e+06 1e+06 1e+06 ...Example #25: More Generic Downloading the Data by Function
The download function can be more generic, with just a few key arguments passed to it. Those arguments can then be derived in a helper function, with the generic function called from inside that.
Example code includes:
genericGetASOSData <- function(fileLoc,
stationID,
startDate,
endDate,
downloadMethod="curl",
baseURL="https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?",
dataFields="all",
dataTZ="Etc%2FUTC",
dataFormat="onlycomma",
dataLatLon="no",
dataMissing="M",
dataTrace="T",
dataDirect="no",
dataType=2
) {
# Get the year, day, and hour of the key dates
y1 <- lubridate::year(startDate)
m1 <- lubridate::month(startDate)
d1 <- lubridate::day(startDate)
y2 <- lubridate::year(endDate)
m2 <- lubridate::month(endDate)
d2 <- lubridate::day(endDate)
# Mimic the string shown below
# https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?station=LAS&data=all&year1=2015&month1=12&day1=31&year2=2017&month2=1&day2=2&tz=Etc%2FUTC&format=onlycomma&latlon=no&missing=M&trace=T&direct=no&report_type=2
useURL <- paste0(baseURL, "station=", stationID) # add the desired station
useURL <- paste0(useURL, "&data=", dataFields) # default is "all
useURL <- paste0(useURL, "&year1=", y1, "&month1=", m1, "&day1=", d1) # Specify start ymd
useURL <- paste0(useURL, "&year2=", y2, "&month2=", m2, "&day2=", d2) # Specify end ymd
useURL <- paste0(useURL, "&tz=", dataTZ) # time zone (default UTC)
useURL <- paste0(useURL, "&format=", dataFormat) # file format (default CSV)
useURL <- paste0(useURL, "&latlon=", dataLatLon) # Whether to include lat-lon (default no)
useURL <- paste0(useURL, "&missing=", dataMissing) # How to handle missing data (default is 'M')
useURL <- paste0(useURL, "&trace=", dataTrace) # How to handle trace data (default is 'T')
useURL <- paste0(useURL, "&direct=", dataDirect) # Whether to directly get the data (default is 'no')
useURL <- paste0(useURL, "&report_type=", dataType) # Whether to get just METAR (2, default)
# Download the file
cat("\nDownloading from:", useURL, "\nDownloading to:", fileLoc, "\n")
download.file(useURL, destfile=fileLoc, method=downloadMethod)
return(TRUE)
}Which can then be called by a function that checks whether the file already exists:
getASOSStationTime <- function(stationID,
startDate=NULL,
endDate=NULL,
analysisYears=NULL,
fileLoc=NULL,
ovrWrite=FALSE,
...) {
# Get the relevant time period for the data
if (is.null(analysisYears) & (is.null(startDate) | is.null(endDate))) {
stop("Must provide either analysisYears or both of startDate and endDate")
}
if (!is.null(startDate) & !is.null(endDate) & !is.null(analysisYears)) {
stop("Should specify EITHER both of startDate and endDate OR analysisYears BUT NOT both")
}
if (is.null(startDate)) {
startDate <- ISOdate(min(analysisYears)-1, 12, 31, hour=0)
endDate <- ISOdate(max(analysisYears)+1, 1, 2, hour=0)
}
# Create the file name
if (!is.null(analysisYears)) {
if (length(analysisYears) == 1) { timeDesc <- analysisYears }
else { timeDesc <- paste0(min(analysisYears), "-", max(analysisYears)) }
} else {
timeDesc <- paste0(lubridate::year(startDate),
str_pad(lubridate::month(startDate), 2, pad="0"),
str_pad(lubridate::day(startDate), 2, pad="0"),
"-",
lubridate::year(endDate),
str_pad(lubridate::month(endDate), 2, pad="0"),
str_pad(lubridate::day(endDate), 2, pad="0")
)
}
if (is.null(fileLoc)) {
fileLoc <- paste0("./RInputFiles/", "metar_k", str_to_lower(stationID), "_", timeDesc, ".txt")
}
cat("\nData for station", stationID, "from", as.character(startDate), "to",
as.character(endDate), "will download to", fileLoc, "\n"
)
if (file.exists(fileLoc) & !ovrWrite) {
stop("File already exists, aborting")
}
genericGetASOSData(fileLoc=fileLoc, stationID=stationID, startDate=startDate, endDate=endDate, ...)
}And the files can then be run (cached to avoid multiple hits to the Iowa State server):
# Get data for EWR for 2016
getASOSStationTime(stationID="EWR", analysisYears=2016)##
## Data for station EWR from 2015-12-31 to 2017-01-02 will download to ./RInputFiles/metar_kewr_2016.txt
##
## Downloading from: https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?station=EWR&data=all&year1=2015&month1=12&day1=31&year2=2017&month2=1&day2=2&tz=Etc%2FUTC&format=onlycomma&latlon=no&missing=M&trace=T&direct=no&report_type=2
## Downloading to: ./RInputFiles/metar_kewr_2016.txt## [1] TRUE# Get data for ATL for 2016-2018
getASOSStationTime(stationID="ATL", analysisYears=2016:2018)##
## Data for station ATL from 2015-12-31 to 2019-01-02 will download to ./RInputFiles/metar_katl_2016-2018.txt
##
## Downloading from: https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?station=ATL&data=all&year1=2015&month1=12&day1=31&year2=2019&month2=1&day2=2&tz=Etc%2FUTC&format=onlycomma&latlon=no&missing=M&trace=T&direct=no&report_type=2
## Downloading to: ./RInputFiles/metar_katl_2016-2018.txt## [1] TRUE# Get data for DFW for 2016-03-31 to 2017-03-01
getASOSStationTime(stationID="DFW",
startDate=ISOdate(2016, 03, 31, hour=0),
endDate=ISOdate(2017, 3, 1, hour=0)
)##
## Data for station DFW from 2016-03-31 to 2017-03-01 will download to ./RInputFiles/metar_kdfw_20160331-20170301.txt
##
## Downloading from: https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?station=DFW&data=all&year1=2016&month1=3&day1=31&year2=2017&month2=3&day2=1&tz=Etc%2FUTC&format=onlycomma&latlon=no&missing=M&trace=T&direct=no&report_type=2
## Downloading to: ./RInputFiles/metar_kdfw_20160331-20170301.txt## [1] TRUEExample #26: Comparative Wind Directions by Location
The processed data files can be compared, with wind directions being one of the comparison sets.
Example code includes:
# Extract the wind direction data from a processed METAR file
getWindDirGroup <- function(keyList, src) {
# Use the fullMETAR data and extract WindDir, WindSpeed, month
windPlotData <- get(keyList)[["fullMETAR"]] %>%
select(WindDir, WindSpeed, month) %>%
mutate(windDirGroup=factor(case_when(WindSpeed==0 ~ "No Wind",
WindDir=="VRB" ~ "Variable",
WindDir %in% c("030", "040", "050", "060") ~ "NE",
WindDir %in% c("070", "080", "090", "100", "110") ~ "E",
WindDir %in% c("120", "130", "140", "150") ~ "SE",
WindDir %in% c("160", "170", "180", "190", "200") ~ "S",
WindDir %in% c("210", "220", "230", "240") ~ "SW",
WindDir %in% c("250", "260", "270", "280", "290") ~ "W",
WindDir %in% c("300", "310", "320", "330") ~ "NW",
WindDir %in% c("340", "350", "360", "010", "020") ~ "N",
TRUE ~ "Error"
) , levels=c("No Wind", "Variable", "Error",
"N", "NE", "E", "SE", "S", "SW", "W", "NW"
)
)
)
# Rempve the errors and calculate percentages by month for the remainder
processedWindData <- windPlotData %>%
filter(windDirGroup != "Error") %>%
group_by(month, windDirGroup) %>%
summarize(n=n()) %>%
ungroup() %>%
group_by(month) %>%
mutate(pct=n/sum(n)) %>%
ungroup() %>%
mutate(src=src)
processedWindData
}
# Consolidate and plot wind data
consolidatePlotWind <- function(files, names) {
consFun <- function(x, y) { getWindDirGroup(keyList=x, src=y) }
boundByRows <- map2_dfr(.x=files, .y=names, .f=consFun)
p <- boundByRows %>%
ggplot(aes(x=month, y=pct, color=src)) +
geom_line(aes(group=src)) +
facet_wrap(~windDirGroup) +
labs(title="Wind Direction Frequency by Month", x="Month", y="Frequency of Wind Observations") +
theme(axis.text.x=element_text(angle=90))
print(p)
boundByRows
}
# Run for 2016 with Lincoln, NE; Las Vegas, NV; and Chicago, IL
cpWind <- consolidatePlotWind(files=c("klnk2016METAR", "klas2016METAR", "kord2016METAR"),
names=c("Lincoln, NE (2016)", "Las Vegas, NV (2016)", "Chicago, IL (2016)")
)
cpWind## # A tibble: 359 x 5
## month windDirGroup n pct src
## <fct> <fct> <int> <dbl> <chr>
## 1 Jan No Wind 76 0.0992 Lincoln, NE (2016)
## 2 Jan Variable 3 0.00392 Lincoln, NE (2016)
## 3 Jan N 179 0.234 Lincoln, NE (2016)
## 4 Jan NE 31 0.0405 Lincoln, NE (2016)
## 5 Jan E 30 0.0392 Lincoln, NE (2016)
## 6 Jan SE 63 0.0822 Lincoln, NE (2016)
## 7 Jan S 174 0.227 Lincoln, NE (2016)
## 8 Jan SW 57 0.0744 Lincoln, NE (2016)
## 9 Jan W 65 0.0849 Lincoln, NE (2016)
## 10 Jan NW 88 0.115 Lincoln, NE (2016)
## # ... with 349 more rowsExample #27: Extracting Precipitation Information from METAR
METAR data include descriptions of the precipitation occuring at any given time. Two of the most common precipitation forms are rain (RA) and SN(). These can occur together, denoted as RASN or SNRA in the METAR.
Further, the precipitation type can be classified using a prefix as light (-), moderate (no prefix), or heavy (+). So, RA would be moderate rain, -SNRA would be a light snow-rain mix, +RA would be heavy rain.
Additionally, the timing of the precipitation event is captured in the remarks using B (begin) and E (end). So, an hourly METAR of RAB20E35B50 would mean rain started at 20 past the hour, ended at 35 past the hour, and began again at 50 past the hour. Since METAR are often taken just before the top of the hour, a four-digit time is used if it is in the ‘previous’ hour; for example, RAB1959E36 in the 2053Z METAR.
We can use the remarks to see when it was raining in the given METAR.
Example code includes:
# Extract the METAR and the date-time from a processed list
procMET <- klas2016METAR$fullMETAR %>%
select(origMETAR, dtime)
# Check whether there are comments for either rain regins (RAB) or rain ends (RAE) and pull all the data
procMET <- procMET %>%
mutate(isRain=grepl("RA[B|E]", origMETAR),
rainData=str_extract(origMETAR, pattern="(RA[B|E]\\d+[0-9BE]*)"),
nBegin=pmax(0, str_count(rainData, "B"), na.rm=TRUE),
nEnd=pmax(0, str_count(rainData, "E"), na.rm=TRUE),
dateUTC=lubridate::date(dtime),
hourUTC=lubridate::hour(dtime)
)
str(procMET)## Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 8 variables:
## $ origMETAR: chr "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## $ dtime : POSIXct, format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## $ isRain : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ rainData : chr NA NA NA NA ...
## $ nBegin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nEnd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dateUTC : Date, format: "2015-12-31" "2015-12-31" ...
## $ hourUTC : int 0 1 2 3 4 5 6 7 8 9 ...# Check the counts of rain beginning and rain ending
procMET %>%
count(isRain, nBegin, nEnd)## # A tibble: 7 x 4
## isRain nBegin nEnd n
## <lgl> <dbl> <dbl> <int>
## 1 FALSE 0 0 8643
## 2 TRUE 0 1 56
## 3 TRUE 1 0 51
## 4 TRUE 1 1 59
## 5 TRUE 1 2 2
## 6 TRUE 2 1 6
## 7 TRUE 2 2 1# Extract all the times when rain began (can be multiple per METAR)
rainMET <- procMET %>%
select(dateUTC, hourUTC, rainData, nBegin, nEnd)
# Confirm the rainMET is unique by dateUTC and hourUTC
dupAns <- rainMET %>%
select(dateUTC, hourUTC) %>%
duplicated() %>%
any()
cat("\nAre there any problems with duplicated keys?", dupAns, "\n")##
## Are there any problems with duplicated keys? FALSE# Extract the matrix of rain beginning data
rainBegin <- rainMET %>%
pull(rainData) %>%
str_extract_all("B\\d+", simplify=TRUE) %>%
as.data.frame(stringsAsFactors=FALSE)
# Extract the matrix of rain ending data
rainEnd <- rainMET %>%
pull(rainData) %>%
str_extract_all("E\\d+", simplify=TRUE) %>%
as.data.frame(stringsAsFactors=FALSE)
# Convert the rain begins data to the hour and minute associated to the UTC
extractTime <- function(x, var, sym="B") {
if (is.na(x[var]) | x[var]=="") {
utcUse <- NA
}
else {
utcUse <- str_replace(x[var], sym, "")
if (str_length(utcUse)==4) {
utcUse <- paste0(x["dateUTC"], " ", utcUse)
} else if (str_length(utcUse)==2) {
utcUse <- paste0(x["dateUTC"], " ", x["hourChar"], utcUse)
} else {
cat("\nCannot parse data: ", x, "\n", x[var], "\n", var, sym, utcUse)
stop()
}
}
}
# Extract the begin times from V1
beginTime1 <- rainBegin %>%
cbind(rainMET[, c("dateUTC", "hourUTC")]) %>%
mutate(hourChar=str_pad(str_trim(as.character(hourUTC)), width=2, pad="0")) %>%
apply(1, FUN=extractTime, var="V1", sym="B")
# Extract the begin times from V2
beginTime2 <- rainBegin %>%
cbind(rainMET[, c("dateUTC", "hourUTC")]) %>%
mutate(hourChar=str_pad(str_trim(as.character(hourUTC)), width=2, pad="0")) %>%
apply(1, FUN=extractTime, var="V2", sym="B")
# Extract the end times from V1
endTime1 <- rainEnd %>%
cbind(rainMET[, c("dateUTC", "hourUTC")]) %>%
mutate(hourChar=str_pad(str_trim(as.character(hourUTC)), width=2, pad="0")) %>%
apply(1, FUN=extractTime, var="V1", sym="E")
# Extract the end times from V2
endTime2 <- rainEnd %>%
cbind(rainMET[, c("dateUTC", "hourUTC")]) %>%
mutate(hourChar=str_pad(str_trim(as.character(hourUTC)), width=2, pad="0")) %>%
apply(1, FUN=extractTime, var="V2", sym="E")
allBegins <-
c(beginTime1[!is.na(beginTime1)], beginTime2[!is.na(beginTime2)]) %>%
lubridate::ymd_hm() %>%
sort()
allEnds <-
c(endTime1[!is.na(endTime1)], endTime2[!is.na(endTime2)]) %>%
lubridate::ymd_hm() %>%
sort()
# Check the each ending is after its associated beginning
endMinusBeg <- allEnds - allBegins## Warning in unclass(time1) - unclass(time2): longer object length is not a
## multiple of shorter object length# Rain is listed as ending at 2016-05-01 00:42 and 2016-04-30 17:38 both for start 2016-04-30 11:20
endMinusBeg <- allEnds[allEnds != lubridate::ymd_hm("2016-05-01 0042")] - allBegins
# Plot the rain durations in minutes
data.frame(minutesRain=as.numeric(endMinusBeg), month=lubridate::month(allBegins)) %>%
ggplot(aes(x=minutesRain)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Plot the rain totals (in hours) by month
data.frame(minutesRain=as.numeric(endMinusBeg), month=lubridate::month(allBegins)) %>%
group_by(month) %>%
summarize(minutesRain=sum(minutesRain), nRain=n()) %>%
ggplot(aes(x=factor(month.abb[month], levels=month.abb[1:12]), y=minutesRain/60)) +
geom_col() +
labs(title="Las Vegas, NV Rainfall (hours) in 2016", y="Hours of Rain", x="Month") +
geom_text(aes(y=2+minutesRain/60, label=round(minutesRain/60, 1)))
Example #28: Function for Extracting Precipitation Information from METAR
The precipitation extraction process can be converted to functions. In addition to being more modular, several additional features can be included:
- Allow to search for other precipitation types, specifically snow (SN) or drizzle (DZ)
- Correct for the isue that a time of 2016-01-04 2359Z is placed in the 206-01-05 bucket by the code above
- Identify areas where the intervals are not sensible or raise significant questions (negative durations, durations longer than 24 hours)
- Check that intervals overlap with the time periods in the METAR that show that precipitation event
Example code includes:
- Extract the precipitation data from a processed METAR file
- Find the begin and end times from this file
- Check for intervals of questionable length and correct as needed
# Extract the precipitation data from a processed METAR file
extractPrecipData <- function(processedFile, pType="RA") {
# Extract the METAR and the date-time from a processed list
procMET <- processedFile[["fullMETAR"]] %>%
select(origMETAR, dtime)
# Check whether there are comments for the desired pType either beginning or ending
keyPattern <- paste0("(", pType, "[B|E]\\d+[0-9BE]*)")
cat("\nRegex search code is:", keyPattern, "\n\n")
procMET <- procMET %>%
mutate(precipData=str_extract(origMETAR, pattern=keyPattern),
isPrecip=grepl(paste0(pType, "[B|E]"), origMETAR),
nBegin=pmax(0, str_count(precipData, "B"), na.rm=TRUE),
nEnd=pmax(0, str_count(precipData, "E"), na.rm=TRUE),
dateUTC=lubridate::date(dtime),
hourUTC=lubridate::hour(dtime)
)
str(procMET)
cat("\n")
# Check the counts of precipitation beginning and rain ending
procMET %>%
count(isPrecip, nBegin, nEnd) %>%
print()
# Check that the file is unique by time
dupAns <- procMET %>%
select(dateUTC, hourUTC) %>%
duplicated %>%
any()
cat("\nAre there any problems with duplicated keys?", dupAns, "\n")
procMET
}
testFileProc <-extractPrecipData(klas2016METAR, pType="RA")##
## Regex search code is: (RA[B|E]\d+[0-9BE]*)
##
## Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 8 variables:
## $ origMETAR : chr "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## $ dtime : POSIXct, format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## $ precipData: chr NA NA NA NA ...
## $ isPrecip : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ nBegin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nEnd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dateUTC : Date, format: "2015-12-31" "2015-12-31" ...
## $ hourUTC : int 0 1 2 3 4 5 6 7 8 9 ...
##
## # A tibble: 7 x 4
## isPrecip nBegin nEnd n
## <lgl> <dbl> <dbl> <int>
## 1 FALSE 0 0 8643
## 2 TRUE 0 1 56
## 3 TRUE 1 0 51
## 4 TRUE 1 1 59
## 5 TRUE 1 2 2
## 6 TRUE 2 1 6
## 7 TRUE 2 2 1
##
## Are there any problems with duplicated keys? FALSENext, a function for extrating beginning and ending times can be written:
# Helper function to extract the beginning and ending times using str_extract_all
getBeginEndTimeMatrix <- function(file, pullVar="precipData", pState="B") {
file %>%
pull(pullVar) %>%
str_extract_all(paste0(pState, "\\d+"), simplify=TRUE) %>%
as.data.frame(stringsAsFactors=FALSE)
}
testBegin <- getBeginEndTimeMatrix(testFileProc, pState="B")
testEnd <- getBeginEndTimeMatrix(testFileProc, pState="E")
if (ncol(testBegin) != 2 | ncol(testEnd) != 2) {
stop("Hard-coded for 2 columns each of begin/end- Fix")
}
# Helper function to convert begin and end time using date and hour
extractTime <- function(x, var, sym="B") {
if (is.na(x[var]) | x[var]=="") {
utcReturn <- NA
}
else {
utcUse <- str_replace(x[var], sym, "")
if (str_length(utcUse)==4) {
utcReturn <- paste0(x["dateUTC"], " ", utcUse)
# If a 4-digit time starts with 23 and is in the 0Z METAR, it is part of the previous day
if(str_sub(utcUse, 1, 2)=="23" & as.numeric(x["hourChar"])==0) {
utcReturn <- paste0(as.Date(x["dateUTC"]) - 1, " ", utcUse)
}
} else if (str_length(utcUse)==2) {
utcReturn <- paste0(x["dateUTC"], " ", x["hourChar"], utcUse)
} else {
cat("\nCannot parse data: ", x, "\n", x[var], "\n", var, sym, utcUse)
stop()
}
}
utcReturn
}
getBeginEndTimeVector <- function(timeExtractFile, origFullFile, extractVar, extractSym) {
timeExtractFile %>%
cbind(origFullFile[, c("dateUTC", "hourUTC")]) %>%
mutate(hourChar=str_pad(str_trim(as.character(hourUTC)), width=2, pad="0")) %>%
apply(1, FUN=extractTime, var=extractVar, sym=extractSym)
}
testBT1 <- getBeginEndTimeVector(testBegin, testFileProc, extractVar="V1", extractSym="B")
testBT2 <- getBeginEndTimeVector(testBegin, testFileProc, extractVar="V2", extractSym="B")
testET1 <- getBeginEndTimeVector(testEnd, testFileProc, extractVar="V1", extractSym="E")
testET2 <- getBeginEndTimeVector(testEnd, testFileProc, extractVar="V2", extractSym="E")
testAllBegins <-
c(testBT1[!is.na(testBT1)], testBT2[!is.na(testBT2)]) %>%
lubridate::ymd_hm() %>%
sort()
testAllEnds <-
c(testET1[!is.na(testET1)], testET2[!is.na(testET2)]) %>%
lubridate::ymd_hm() %>%
sort()Next, create the time intervals vector, with the capability to change the start state, check the intervals, exclude times as needed, and compare to raw METAR.
Example code includes:
# Function to create the time intervals data
createPrecipInterval <- function(endVector, beginVector, endExclude=c(), beginExclude=c(),
sState=FALSE, nMinPrint=1, maxProb=1000, nMaxPrint=1
) {
# If the starting state is one of precipitation, allow it to 'burn in' by deleting the first end time
if(sState) { endVector <- endVector[2:length(endVector)] }
# Create the interval data
endsUse <- endVector[!(endVector %in% lubridate::ymd_hm(endExclude))]
beginsUse <- beginVector[!(beginVector %in% lubridate::ymd_hm(beginExclude))]
intervalData <- endsUse - beginsUse
# Show a summary of the interval data
print(summary(as.numeric(intervalData)))
# If there are any non-positive intervals, print the data causing the first of them
if (min(as.numeric(intervalData)) <= 0) {
cat("\nProblem Detected - Intervals are not positive. Data to help investigate\n")
posns <- which(as.numeric(intervalData) <= 0)
posns <- max(1, posns[1]-5):min(length(beginsUse), length(endsUse), posns[nMinPrint]+5)
cat("\nVector of Begins\n")
print(lubridate::as_datetime(beginsUse[posns]))
cat("\nVector of Ends\n")
print(lubridate::as_datetime(endsUse[posns]))
cat("\n")
}
# If there are any very long positive intervals, print the data causing the first five of them
if (max(as.numeric(intervalData)) >= maxProb) {
cat("\nPotential problem Detected - very long. Data to help investigate\n")
posns <- which(as.numeric(intervalData) >= maxProb)
cat("\nPositions with problems are:", posns)
posns <- max(1, posns[1]-5):min(length(beginsUse), length(endsUse), posns[min(length(posns), nMaxPrint)]+5)
cat("\nVector of Begins\n")
print(lubridate::as_datetime(beginsUse[posns]))
cat("\nVector of Ends\n")
print(lubridate::as_datetime(endsUse[posns]))
cat("\n")
}
# Return the interval data
intervalData
}
# Run a full pass for Las Vegas Rain
testIntervals <- createPrecipInterval(testAllEnds, testAllBegins)## Warning in unclass(time1) - unclass(time2): longer object length is not a
## multiple of shorter object length## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -41221.0 -454.5 -16.0 1382.7 30.0 520731.0
##
## Problem Detected - Intervals are not positive. Data to help investigate
##
## Vector of Begins
## [1] "2016-04-28 21:33:00 UTC" "2016-04-29 00:34:00 UTC"
## [3] "2016-04-29 03:27:00 UTC" "2016-04-30 08:51:00 UTC"
## [5] "2016-04-30 11:20:00 UTC" "2016-05-01 04:16:00 UTC"
## [7] "2016-05-01 04:55:00 UTC" "2016-05-06 14:35:00 UTC"
## [9] "2016-05-06 19:20:00 UTC" "2016-05-07 07:24:00 UTC"
## [11] "2016-05-07 22:49:00 UTC"
##
## Vector of Ends
## [1] "2016-04-28 21:42:00 UTC" "2016-04-29 00:58:00 UTC"
## [3] "2016-04-29 03:51:00 UTC" "2016-04-30 09:53:00 UTC"
## [5] "2016-04-30 17:38:00 UTC" "2016-05-01 00:42:00 UTC"
## [7] "2016-05-01 04:31:00 UTC" "2016-05-01 05:10:00 UTC"
## [9] "2016-05-06 15:12:00 UTC" "2016-05-06 19:30:00 UTC"
## [11] "2016-05-07 07:45:00 UTC"
##
##
## Potential problem Detected - very long. Data to help investigate
##
## Positions with problems are: 127
## Vector of Begins
## [1] "2016-12-25 00:16:00 UTC" "2016-12-30 22:09:00 UTC"
## [3] "2016-12-30 23:44:00 UTC" "2016-12-31 03:59:00 UTC"
## [5] "2016-12-31 05:45:00 UTC"
##
## Vector of Ends
## [1] "2016-12-24 14:06:00 UTC" "2016-12-25 00:32:00 UTC"
## [3] "2016-12-30 22:35:00 UTC" "2016-12-31 00:04:00 UTC"
## [5] "2016-12-31 04:12:00 UTC"testIntervals <- createPrecipInterval(testAllEnds, testAllBegins, endExclude=c("2016-05-01 0042"))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 16.00 29.00 52.15 57.75 502.00createPrecipIntervalPlots <- function(intervalData, beginsData, titleText, yAxisText,
beginExclude=c(), returnPlotsAndData=FALSE
) {
# Exclude any data from begins as needed
beginsData <- beginsData[!(beginsData %in% lubridate::ymd_hm(beginExclude))]
# Create a plotting data frame
histFrame <- data.frame(minutesPrecip=as.numeric(intervalData),
month=lubridate::month(beginsData),
rainDate=lubridate::date(beginsData)
) %>%
mutate(hoursPrecip=minutesPrecip/60)
# Plot the precipitation durations in hours
p1 <- histFrame %>%
ggplot(aes(x=hoursPrecip)) +
geom_histogram() +
labs(title=titleText, x=yAxisText,
subtitle="Distribution of hours per unique precipitation event"
)
print(p1)
# Plot the precipitation by day in hours
p2 <- histFrame %>%
group_by(rainDate) %>%
summarize(hoursPrecip=sum(hoursPrecip)) %>%
ggplot(aes(x=hoursPrecip)) +
geom_histogram() +
labs(title=titleText, x=yAxisText,
subtitle="Distribution of hours per day of precipitation (on days when 1+ minutes occurred)"
)
print(p2)
# Plot the rain totals (in hours) by month
# Create a data frame of all months and merge in precipitation data as an where available (0 otherwise)
monthFrame <- histFrame %>%
group_by(month) %>%
summarize(minutesPrecip=sum(minutesPrecip), hoursPrecip=sum(hoursPrecip), nPrecip=n()) %>%
right_join(data.frame(month=1:12, monthName=month.abb[1:12]), by="month") %>%
tidyr::replace_na(list(minutesPrecip=0, hoursPrecip=0, nPrecip=0))
# print(monthFrame)
p3 <- monthFrame %>%
ggplot(aes(x=factor(monthName, levels=month.abb[1:12]), y=hoursPrecip)) +
geom_col() +
labs(title=titleText, y=yAxisText, x="") +
geom_text(aes(y=2 + hoursPrecip, label=round(hoursPrecip, 1)))
print(p3)
if (returnPlotsAndData) {
list(histFrame=histFrame, monthFrame=monthFrame, p1=p1, p2=p2, p3=p3)
} else {
NULL
}
}
# Plots for Las Vegas Rain
createPrecipIntervalPlots(testIntervals, testAllBegins,
titleText="Las Vegas, NV Rainfall (hours) in 2016", yAxisText="Hours of Rain"
)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## NULLExample #29: Combining Functions and Extending to Other Locales and Precipitation Types
The functions can be combined, and the approach then extended to other locales and precipitation types.
Example code includes:
# Combining all the functions in one place
runFullPrecipExtraction <- function(df,
pType,
titleText,
yAxisText,
endExclude=c(),
beginExclude=c(),
maxProb=1000,
sState=FALSE,
makePlots=TRUE,
returnPlotsAndData=FALSE
) {
# Extract the precipitation data from a specified processed METAR file
testFileProc <-extractPrecipData(df, pType=pType)
# Confirm that the two-column specification is met (should relax hard-coding on this)
testBegin <- getBeginEndTimeMatrix(testFileProc, pState="B")
testEnd <- getBeginEndTimeMatrix(testFileProc, pState="E")
if (ncol(testBegin) != 2 | ncol(testEnd) != 2) {
stop("Hard-coded for 2 columns each of begin/end- Fix")
}
# Extract the beginning and ending information
testBT1 <- getBeginEndTimeVector(testBegin, testFileProc, extractVar="V1", extractSym="B")
testBT2 <- getBeginEndTimeVector(testBegin, testFileProc, extractVar="V2", extractSym="B")
testET1 <- getBeginEndTimeVector(testEnd, testFileProc, extractVar="V1", extractSym="E")
testET2 <- getBeginEndTimeVector(testEnd, testFileProc, extractVar="V2", extractSym="E")
testAllBegins <-
c(testBT1[!is.na(testBT1)], testBT2[!is.na(testBT2)]) %>%
lubridate::ymd_hm() %>%
sort()
testAllEnds <-
c(testET1[!is.na(testET1)], testET2[!is.na(testET2)]) %>%
lubridate::ymd_hm() %>%
sort()
# Create the intervals
testIntervals <- createPrecipInterval(testAllEnds, testAllBegins, endExclude=endExclude,
beginExclude=beginExclude, maxProb=maxProb, sState=sState)
# Create the precipitation plots
plotOut <- NULL
if (makePlots) {
plotOut <- createPrecipIntervalPlots(testIntervals, testAllBegins, titleText=titleText,
yAxisText=yAxisText, beginExclude=beginExclude,
returnPlotsAndData=returnPlotsAndData
)
}
if (!returnPlotsAndData) { plotOut <- NULL }
# Return all of the key files, along with the parameters used
keyParams <- list(fileName=deparse(substitute(df)), pType=pType,
endExclude=endExclude, beginExclude=beginExclude,
maxProb=maxProb, sState=sState
)
list(keyParams=keyParams,
testFileProc=testFileProc,
testAllBegins=testAllBegins[!(testAllBegins %in% lubridate::ymd_hm(beginExclude))],
testAllEnds=testAllEnds[!(testAllEnds %in% lubridate::ymd_hm(endExclude))],
testIntervals=testIntervals,
plotOut=plotOut
)
}The full function can then be run to replicate the Las Vegas, NV 2016 rain plots:
# Run for Las Vegas, NV 2016 rainfall
klasRain2016 <- runFullPrecipExtraction(klas2016METAR,
pType="RA",
titleText="Las Vegas, NV Rainfall (hours) in 2016",
yAxisText="Hours of Rain",
endExclude=c("2016-05-01 0042"),
beginExclude=c(),
maxProb=1000,
sState=FALSE
)##
## Regex search code is: (RA[B|E]\d+[0-9BE]*)
##
## Classes 'tbl_df', 'tbl' and 'data.frame': 8818 obs. of 8 variables:
## $ origMETAR : chr "KLAS 310056Z 10003KT 10SM CLR 08/M08 A3009 RMK AO2 SLP196 T00831078" "KLAS 310156Z 01005KT 10SM CLR 07/M08 A3011 RMK AO2 SLP204 T00671083" "KLAS 310256Z 01005KT 10SM CLR 06/M09 A3013 RMK AO2 SLP210 T00611089 53011" "KLAS 310356Z 34005KT 10SM CLR 05/M09 A3014 RMK AO2 SLP215 T00501089" ...
## $ dtime : POSIXct, format: "2015-12-31 00:56:00" "2015-12-31 01:56:00" ...
## $ precipData: chr NA NA NA NA ...
## $ isPrecip : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ nBegin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nEnd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dateUTC : Date, format: "2015-12-31" "2015-12-31" ...
## $ hourUTC : int 0 1 2 3 4 5 6 7 8 9 ...
##
## # A tibble: 7 x 4
## isPrecip nBegin nEnd n
## <lgl> <dbl> <dbl> <int>
## 1 FALSE 0 0 8643
## 2 TRUE 0 1 56
## 3 TRUE 1 0 51
## 4 TRUE 1 1 59
## 5 TRUE 1 2 2
## 6 TRUE 2 1 6
## 7 TRUE 2 2 1
##
## Are there any problems with duplicated keys? FALSE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 16.00 29.00 52.15 57.75 502.00## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Confirm that results are the same as when run outside the function
identical(testIntervals, klasRain2016$testIntervals)## [1] TRUEidentical(testFileProc, klasRain2016$testFileProc)## [1] TRUEidentical(testAllBegins, klasRain2016$testAllBegins)## [1] TRUEidentical(testAllEnds, klasRain2016$testAllEnds)## [1] FALSE# Show the key parameters used
klasRain2016$keyParams## $fileName
## [1] "klas2016METAR"
##
## $pType
## [1] "RA"
##
## $endExclude
## [1] "2016-05-01 0042"
##
## $beginExclude
## NULL
##
## $maxProb
## [1] 1000
##
## $sState
## [1] FALSEThe full functions can then be explored for Chicago, IL 2016 rain plots:
# Run for Chicago, IL 2016 rainfall - run with no plots while debugging begin/end exclude
kordRain2016 <- runFullPrecipExtraction(kord2016METAR,
pType="RA",
titleText="Chicago, IL Rainfall (hours) in 2016",
yAxisText="Hours of Rain",
endExclude=c(),
beginExclude=c(),
maxProb=1440,
sState=FALSE,
makePlots=FALSE
)##
## Regex search code is: (RA[B|E]\d+[0-9BE]*)
##
## Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 8 variables:
## $ origMETAR : chr "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## $ precipData: chr NA NA NA NA ...
## $ isPrecip : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ nBegin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nEnd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dateUTC : Date, format: "2015-12-31" "2015-12-31" ...
## $ hourUTC : int 0 1 2 3 4 5 6 7 8 9 ...
##
## # A tibble: 7 x 4
## isPrecip nBegin nEnd n
## <lgl> <dbl> <dbl> <int>
## 1 FALSE 0 0 8256
## 2 TRUE 0 1 179
## 3 TRUE 1 0 177
## 4 TRUE 1 1 168
## 5 TRUE 1 2 14
## 6 TRUE 2 1 14
## 7 TRUE 2 2 7
##
## Are there any problems with duplicated keys? FALSE## Warning in unclass(time1) - unclass(time2): longer object length is not a
## multiple of shorter object length## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -31587.0 -2464.5 -355.0 703.3 -121.0 509393.0
##
## Problem Detected - Intervals are not positive. Data to help investigate
##
## Vector of Begins
## [1] "2016-02-02 23:56:00 UTC" "2016-02-03 18:13:00 UTC"
## [3] "2016-02-03 22:40:00 UTC" "2016-02-29 01:17:00 UTC"
## [5] "2016-03-01 02:48:00 UTC" "2016-03-01 04:36:00 UTC"
## [7] "2016-03-01 09:44:00 UTC" "2016-03-01 11:01:00 UTC"
## [9] "2016-03-07 16:43:00 UTC" "2016-03-08 10:46:00 UTC"
## [11] "2016-03-09 05:56:00 UTC"
##
## Vector of Ends
## [1] "2016-02-03 01:25:00 UTC" "2016-02-03 18:43:00 UTC"
## [3] "2016-02-03 23:00:00 UTC" "2016-02-29 02:39:00 UTC"
## [5] "2016-03-01 03:11:00 UTC" "2016-03-01 04:14:00 UTC"
## [7] "2016-03-01 05:30:00 UTC" "2016-03-01 10:17:00 UTC"
## [9] "2016-03-01 11:23:00 UTC" "2016-03-07 17:49:00 UTC"
## [11] "2016-03-08 10:55:00 UTC"
##
##
## Potential problem Detected - very long. Data to help investigate
##
## Positions with problems are: 402 403
## Vector of Begins
## [1] "2016-12-25 22:11:00 UTC" "2016-12-26 04:56:00 UTC"
## [3] "2016-12-26 08:23:00 UTC" "2016-12-26 08:57:00 UTC"
## [5] "2016-12-26 12:46:00 UTC"
##
## Vector of Ends
## [1] "2016-12-23 23:32:00 UTC" "2016-12-24 03:43:00 UTC"
## [3] "2016-12-25 23:40:00 UTC" "2016-12-26 05:20:00 UTC"
## [5] "2016-12-26 08:37:00 UTC"# Exclude the first problem end time - 2016-03-01 04:14:00 UTC
kordRain2016 <- runFullPrecipExtraction(kord2016METAR,
pType="RA",
titleText="Chicago, IL Rainfall (hours) in 2016",
yAxisText="Hours of Rain",
endExclude=c("2016-03-01 0414"),
beginExclude=c(),
maxProb=1440,
sState=FALSE,
makePlots=FALSE
)##
## Regex search code is: (RA[B|E]\d+[0-9BE]*)
##
## Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 8 variables:
## $ origMETAR : chr "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## $ precipData: chr NA NA NA NA ...
## $ isPrecip : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ nBegin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nEnd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dateUTC : Date, format: "2015-12-31" "2015-12-31" ...
## $ hourUTC : int 0 1 2 3 4 5 6 7 8 9 ...
##
## # A tibble: 7 x 4
## isPrecip nBegin nEnd n
## <lgl> <dbl> <dbl> <int>
## 1 FALSE 0 0 8256
## 2 TRUE 0 1 179
## 3 TRUE 1 0 177
## 4 TRUE 1 1 168
## 5 TRUE 1 2 14
## 6 TRUE 2 1 14
## 7 TRUE 2 2 7
##
## Are there any problems with duplicated keys? FALSE## Warning in unclass(time1) - unclass(time2): longer object length is not a
## multiple of shorter object length## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -30696.0 -253.0 -42.0 515.3 5.0 509428.0
##
## Problem Detected - Intervals are not positive. Data to help investigate
##
## Vector of Begins
## [1] "2016-03-24 03:38:00 UTC" "2016-03-24 05:49:00 UTC"
## [3] "2016-03-24 07:38:00 UTC" "2016-03-24 11:32:00 UTC"
## [5] "2016-03-24 14:41:00 UTC" "2016-03-27 19:59:00 UTC"
## [7] "2016-03-30 13:42:00 UTC" "2016-03-30 16:34:00 UTC"
## [9] "2016-03-30 21:13:00 UTC" "2016-03-30 23:27:00 UTC"
## [11] "2016-03-31 01:09:00 UTC"
##
## Vector of Ends
## [1] "2016-03-24 05:09:00 UTC" "2016-03-24 05:51:00 UTC"
## [3] "2016-03-24 08:20:00 UTC" "2016-03-24 14:01:00 UTC"
## [5] "2016-03-24 18:41:00 UTC" "2016-03-24 22:14:00 UTC"
## [7] "2016-03-28 00:49:00 UTC" "2016-03-30 13:50:00 UTC"
## [9] "2016-03-30 16:59:00 UTC" "2016-03-30 21:25:00 UTC"
## [11] "2016-03-31 00:20:00 UTC"
##
##
## Potential problem Detected - very long. Data to help investigate
##
## Positions with problems are: 402
## Vector of Begins
## [1] "2016-12-25 22:11:00 UTC" "2016-12-26 04:56:00 UTC"
## [3] "2016-12-26 08:23:00 UTC" "2016-12-26 08:57:00 UTC"
## [5] "2016-12-26 12:46:00 UTC"
##
## Vector of Ends
## [1] "2016-12-24 03:43:00 UTC" "2016-12-25 23:40:00 UTC"
## [3] "2016-12-26 05:20:00 UTC" "2016-12-26 08:37:00 UTC"
## [5] "2016-12-26 12:23:00 UTC"# Exclude the second problem end time - 2016-03-24 22:14:00 UTC
kordRain2016 <- runFullPrecipExtraction(kord2016METAR,
pType="RA",
titleText="Chicago, IL Rainfall (hours) in 2016",
yAxisText="Hours of Rain",
endExclude=c("2016-03-01 0414", "2016-03-24 2214"),
beginExclude=c(),
maxProb=1440,
sState=FALSE,
makePlots=FALSE
)##
## Regex search code is: (RA[B|E]\d+[0-9BE]*)
##
## Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 8 variables:
## $ origMETAR : chr "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## $ precipData: chr NA NA NA NA ...
## $ isPrecip : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ nBegin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nEnd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dateUTC : Date, format: "2015-12-31" "2015-12-31" ...
## $ hourUTC : int 0 1 2 3 4 5 6 7 8 9 ...
##
## # A tibble: 7 x 4
## isPrecip nBegin nEnd n
## <lgl> <dbl> <dbl> <int>
## 1 FALSE 0 0 8256
## 2 TRUE 0 1 179
## 3 TRUE 1 0 177
## 4 TRUE 1 1 168
## 5 TRUE 1 2 14
## 6 TRUE 2 1 14
## 7 TRUE 2 2 7
##
## Are there any problems with duplicated keys? FALSE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.0 15.0 39.0 239.5 119.0 10708.0
##
## Potential problem Detected - very long. Data to help investigate
##
## Positions with problems are: 202 204 206 207 211 213 214 215 224 227 236 240 244
## Vector of Begins
## [1] "2016-07-01 03:25:00 UTC" "2016-07-06 07:42:00 UTC"
## [3] "2016-07-07 22:34:00 UTC" "2016-07-13 13:51:00 UTC"
## [5] "2016-07-13 21:47:00 UTC" "2016-07-13 23:47:00 UTC"
## [7] "2016-07-15 19:16:00 UTC" "2016-07-15 21:06:00 UTC"
## [9] "2016-07-17 13:44:00 UTC" "2016-07-18 04:11:00 UTC"
## [11] "2016-07-22 01:46:00 UTC"
##
## Vector of Ends
## [1] "2016-07-01 03:38:00 UTC" "2016-07-06 14:08:00 UTC"
## [3] "2016-07-08 00:36:00 UTC" "2016-07-13 22:07:00 UTC"
## [5] "2016-07-14 00:54:00 UTC" "2016-07-15 20:39:00 UTC"
## [7] "2016-07-15 22:27:00 UTC" "2016-07-17 14:38:00 UTC"
## [9] "2016-07-18 04:44:00 UTC" "2016-07-22 06:34:00 UTC"
## [11] "2016-07-24 01:00:00 UTC"# Exclude the begin time that creates a 3-day continuous rainfall - 2016-07-13 2347
kordRain2016 <- runFullPrecipExtraction(kord2016METAR,
pType="RA",
titleText="Chicago, IL Rainfall (hours) in 2016",
yAxisText="Hours of Rain",
endExclude=c("2016-03-01 0414", "2016-03-24 2214"),
beginExclude=c("2016-07-13 2347"),
maxProb=1440,
sState=FALSE,
makePlots=FALSE
)##
## Regex search code is: (RA[B|E]\d+[0-9BE]*)
##
## Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 8 variables:
## $ origMETAR : chr "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## $ precipData: chr NA NA NA NA ...
## $ isPrecip : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ nBegin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nEnd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dateUTC : Date, format: "2015-12-31" "2015-12-31" ...
## $ hourUTC : int 0 1 2 3 4 5 6 7 8 9 ...
##
## # A tibble: 7 x 4
## isPrecip nBegin nEnd n
## <lgl> <dbl> <dbl> <int>
## 1 FALSE 0 0 8256
## 2 TRUE 0 1 179
## 3 TRUE 1 0 177
## 4 TRUE 1 1 168
## 5 TRUE 1 2 14
## 6 TRUE 2 1 14
## 7 TRUE 2 2 7
##
## Are there any problems with duplicated keys? FALSE## Warning in unclass(time1) - unclass(time2): longer object length is not a
## multiple of shorter object length## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -30696.0 -44.0 13.0 915.4 54.0 509428.0
##
## Problem Detected - Intervals are not positive. Data to help investigate
##
## Vector of Begins
## [1] "2016-08-25 09:29:00 UTC" "2016-08-27 04:07:00 UTC"
## [3] "2016-08-27 06:24:00 UTC" "2016-08-27 10:40:00 UTC"
## [5] "2016-08-27 14:06:00 UTC" "2016-08-29 20:59:00 UTC"
## [7] "2016-08-29 23:15:00 UTC" "2016-08-30 10:23:00 UTC"
## [9] "2016-09-01 20:08:00 UTC" "2016-09-06 19:54:00 UTC"
## [11] "2016-09-07 17:13:00 UTC"
##
## Vector of Ends
## [1] "2016-08-25 09:39:00 UTC" "2016-08-27 05:07:00 UTC"
## [3] "2016-08-27 10:21:00 UTC" "2016-08-27 13:20:00 UTC"
## [5] "2016-08-27 14:48:00 UTC" "2016-08-29 20:48:00 UTC"
## [7] "2016-08-29 21:16:00 UTC" "2016-08-30 00:38:00 UTC"
## [9] "2016-08-30 13:56:00 UTC" "2016-09-01 20:18:00 UTC"
## [11] "2016-09-06 20:11:00 UTC"
##
##
## Potential problem Detected - very long. Data to help investigate
##
## Positions with problems are: 401
## Vector of Begins
## [1] "2016-12-25 22:11:00 UTC" "2016-12-26 04:56:00 UTC"
## [3] "2016-12-26 08:23:00 UTC" "2016-12-26 08:57:00 UTC"
## [5] "2016-12-26 12:46:00 UTC"
##
## Vector of Ends
## [1] "2016-12-24 03:43:00 UTC" "2016-12-25 23:40:00 UTC"
## [3] "2016-12-26 05:20:00 UTC" "2016-12-26 08:37:00 UTC"
## [5] "2016-12-26 12:23:00 UTC"# Exclude the third problem end time - 2016-08-29 20:48:00 UTC
# As this is the final data integrity issue, show the plots
kordRain2016 <- runFullPrecipExtraction(kord2016METAR,
pType="RA",
titleText="Chicago, IL Rainfall (hours) in 2016",
yAxisText="Hours of Rain",
endExclude=c("2016-03-01 0414", "2016-03-24 2214",
"2016-08-29 2048"
),
beginExclude=c("2016-07-13 2347"),
maxProb=1440,
sState=FALSE,
makePlots=TRUE
)##
## Regex search code is: (RA[B|E]\d+[0-9BE]*)
##
## Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 8 variables:
## $ origMETAR : chr "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## $ precipData: chr NA NA NA NA ...
## $ isPrecip : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ nBegin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nEnd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dateUTC : Date, format: "2015-12-31" "2015-12-31" ...
## $ hourUTC : int 0 1 2 3 4 5 6 7 8 9 ...
##
## # A tibble: 7 x 4
## isPrecip nBegin nEnd n
## <lgl> <dbl> <dbl> <int>
## 1 FALSE 0 0 8256
## 2 TRUE 0 1 179
## 3 TRUE 1 0 177
## 4 TRUE 1 1 168
## 5 TRUE 1 2 14
## 6 TRUE 2 1 14
## 7 TRUE 2 2 7
##
## Are there any problems with duplicated keys? FALSE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 14.00 33.50 71.33 86.00 1243.00## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Show the key parameters used
kordRain2016$keyParams## $fileName
## [1] "kord2016METAR"
##
## $pType
## [1] "RA"
##
## $endExclude
## [1] "2016-03-01 0414" "2016-03-24 2214" "2016-08-29 2048"
##
## $beginExclude
## [1] "2016-07-13 2347"
##
## $maxProb
## [1] 1440
##
## $sState
## [1] FALSEThe full functions can then be explored for Chicago, IL 2016 snow plots:
# Run for Chicago, IL 2016 snowfall - run with no plots while debugging begin/end exclude
kordSnow2016 <- runFullPrecipExtraction(kord2016METAR,
pType="SN",
titleText="Chicago, IL Snowfall (hours) in 2016",
yAxisText="Hours of Snow",
endExclude=c(),
beginExclude=c(),
maxProb=1440,
sState=FALSE,
makePlots=FALSE
)##
## Regex search code is: (SN[B|E]\d+[0-9BE]*)
##
## Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 8 variables:
## $ origMETAR : chr "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## $ precipData: chr NA NA NA NA ...
## $ isPrecip : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ nBegin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nEnd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dateUTC : Date, format: "2015-12-31" "2015-12-31" ...
## $ hourUTC : int 0 1 2 3 4 5 6 7 8 9 ...
##
## # A tibble: 6 x 4
## isPrecip nBegin nEnd n
## <lgl> <dbl> <dbl> <int>
## 1 FALSE 0 0 8630
## 2 TRUE 0 1 69
## 3 TRUE 1 0 62
## 4 TRUE 1 1 44
## 5 TRUE 1 2 4
## 6 TRUE 2 1 6
##
## Are there any problems with duplicated keys? FALSE## Warning in unclass(time1) - unclass(time2): longer object length is not a
## multiple of shorter object length## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -348118 -2604 -97 7747 33 520190
##
## Problem Detected - Intervals are not positive. Data to help investigate
##
## Vector of Begins
## [1] "2016-02-09 00:53:00 UTC" "2016-02-09 02:48:00 UTC"
## [3] "2016-02-09 18:08:00 UTC" "2016-02-09 18:55:00 UTC"
## [5] "2016-02-09 23:25:00 UTC" "2016-02-10 02:36:00 UTC"
## [7] "2016-02-10 04:19:00 UTC" "2016-02-12 13:32:00 UTC"
## [9] "2016-02-12 14:28:00 UTC" "2016-02-12 17:15:00 UTC"
## [11] "2016-02-14 17:06:00 UTC"
##
## Vector of Ends
## [1] "2016-02-09 01:46:00 UTC" "2016-02-09 17:19:00 UTC"
## [3] "2016-02-09 18:41:00 UTC" "2016-02-09 22:30:00 UTC"
## [5] "2016-02-09 23:33:00 UTC" "2016-02-10 02:16:00 UTC"
## [7] "2016-02-10 03:38:00 UTC" "2016-02-10 04:31:00 UTC"
## [9] "2016-02-12 13:45:00 UTC" "2016-02-12 16:00:00 UTC"
## [11] "2016-02-12 17:45:00 UTC"
##
##
## Potential problem Detected - very long. Data to help investigate
##
## Positions with problems are: 123 124 125 126 127
## Vector of Begins
## [1] "2016-12-23 20:18:00 UTC" "2016-12-23 23:01:00 UTC"
## [3] "2016-12-24 03:43:00 UTC" "2016-12-29 18:33:00 UTC"
## [5] "2016-12-30 02:46:00 UTC"
##
## Vector of Ends
## [1] "2016-12-17 09:46:00 UTC" "2016-12-17 20:09:00 UTC"
## [3] "2016-12-18 11:07:00 UTC" "2016-12-23 17:50:00 UTC"
## [5] "2016-12-23 20:34:00 UTC"# After investigation, there are five extraneous end times in the Chicago snowfall data - exclude them
# Create the plots
kordSnow2016 <- runFullPrecipExtraction(kord2016METAR,
pType="SN",
titleText="Chicago, IL Snowfall (hours) in 2016",
yAxisText="Hours of Snow",
endExclude=c("2016-02-10 0216", "2016-03-24 2331",
"2016-04-08 2300", "2016-04-09 0446",
"2016-12-24 0309"
),
beginExclude=c(),
maxProb=1440,
sState=FALSE,
makePlots=TRUE
)##
## Regex search code is: (SN[B|E]\d+[0-9BE]*)
##
## Classes 'tbl_df', 'tbl' and 'data.frame': 8815 obs. of 8 variables:
## $ origMETAR : chr "KORD 310051Z 26008KT 8SM OVC016 M03/M06 A3021 RMK AO2 SLP239 T10281056" "KORD 310151Z 26008KT 9SM OVC016 M03/M06 A3021 RMK AO2 SLP240 T10281056" "KORD 310251Z 25008KT 10SM OVC015 M03/M06 A3021 RMK AO2 SLP239 T10281061 50004" "KORD 310351Z 24009KT 10SM OVC019 M03/M06 A3022 RMK AO2 SLP242 T10281061" ...
## $ dtime : POSIXct, format: "2015-12-31 00:51:00" "2015-12-31 01:51:00" ...
## $ precipData: chr NA NA NA NA ...
## $ isPrecip : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ nBegin : num 0 0 0 0 0 0 0 0 0 0 ...
## $ nEnd : num 0 0 0 0 0 0 0 0 0 0 ...
## $ dateUTC : Date, format: "2015-12-31" "2015-12-31" ...
## $ hourUTC : int 0 1 2 3 4 5 6 7 8 9 ...
##
## # A tibble: 6 x 4
## isPrecip nBegin nEnd n
## <lgl> <dbl> <dbl> <int>
## 1 FALSE 0 0 8630
## 2 TRUE 0 1 69
## 3 TRUE 1 0 62
## 4 TRUE 1 1 44
## 5 TRUE 1 2 4
## 6 TRUE 2 1 6
##
## Are there any problems with duplicated keys? FALSE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 20.25 47.00 136.29 152.25 1016.00## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Show the key parameters used
kordSnow2016$keyParams## $fileName
## [1] "kord2016METAR"
##
## $pType
## [1] "SN"
##
## $endExclude
## [1] "2016-02-10 0216" "2016-03-24 2331" "2016-04-08 2300" "2016-04-09 0446"
## [5] "2016-12-24 0309"
##
## $beginExclude
## NULL
##
## $maxProb
## [1] 1440
##
## $sState
## [1] FALSEExample #30: Checking Intervals for Consistency
The intervals created above can be compared against the hourly METAR observations for consistency. In general, RAE53 would lead to no precipitation recorded at 53Z while RAB53 would lead to precipitation recorded at 53Z. So, the intervals created should be reduced by 1 for purposes of overlap analysis.
Example code includes:
library(lubridate) # lubridate has the %within% function and creates and checks intervals##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## dateintervalConsistency <- function(lst, pType){
# Extract the beginning and interval times
begins <- lst[["testAllBegins"]]
ends <- lst[["testAllEnds"]]
durs <- lst[["testIntervals"]]
# Create intervals from the raw list file
precipInts <- interval(begins, begins + durs - 1)
# Extract the METAR and date-time information
metar <- lst[["testFileProc"]][["origMETAR"]]
dtime <- lst[["testFileProc"]][["dtime"]]
# Take each METAR observation and check two factors
# Is the precipitation type recorded in that METAR?
# Does that METAR fall in any of the intervals?
precipMETAR <- grepl(paste0(pType, ".*RMK"), metar)
intMETAR <- sapply(dtime, FUN=function(x) {x %within% precipInts %>% any()})
# Check for the consistency of the observations and print the mismatches
print(table(precipMETAR, intMETAR))
mism <- which(precipMETAR != intMETAR)
if (length(mism) == 0) {
cat("\nFull matches between METAR observations and intervals\n")
} else {
for (x in mism) {
cat("\nMismatch at time", strftime(dtime[x], format="%Y-%m-%d %H:%M"), "UTC\n")
print(metar[max(1, x-2):min(length(metar), x+2)])
}
}
list(precipInts=precipInts, mismatches=mism, mismatchTimes=dtime[mism])
}
# Mismatch due to TSRA without RAB on 2016-04-30
tmp <- intervalConsistency(klasRain2016, pType="RA")## intMETAR
## precipMETAR FALSE TRUE
## FALSE 8710 0
## TRUE 1 107
##
## Mismatch at time 2016-04-30 18:56 UTC
## [1] "KLAS 302156Z 01012G18KT 10SM FEW045 BKN060 BKN110 18/07 A2973 RMK AO2 SLP055 T01830072 $"
## [2] "KLAS 302256Z 04009KT 10SM FEW050 BKN090 OVC110 18/07 A2972 RMK AO2 SLP054 SH DSNT SE-S T01780067 $"
## [3] "KLAS 302356Z 04026G31KT 6SM TSRA SCT035 BKN080CB OVC100 16/10 A2978 RMK AO2 PK WND 04031/2356 PRESRR SLP075 OCNL LTGICCG N TS N-NE MOV SW P0000 60000 T01610100 10189 20106 53013 $"
## [4] "KLAS 010056Z 17004KT 10SM FEW026 BKN055 BKN100 14/11 A2976 RMK AO2 PK WND 04033/2357 SLP070 TSE19 MOV SW RAE42 P0037 T01390106 $"
## [5] "KLAS 010156Z 27007KT 10SM FEW050 BKN095 BKN220 14/09 A2975 RMK AO2 SLP068 T01390094 $"# Mismatches due to malformatted METAR with missing data on 2016-07-13
# Mismatch due to RA vs FZRA on 2016-02-29
# Mismatch due to -RASN without RAB on 2016-03-24
# Mismatch due to TSRA without RAB on 2016-08-29
tmp <- intervalConsistency(kordRain2016, pType="RA")## intMETAR
## precipMETAR FALSE TRUE
## FALSE 8342 4
## TRUE 3 466
##
## Mismatch at time 2016-02-29 21:51 UTC
## [1] "KORD 010151Z 06012KT 10SM -DZ OVC070 00/M06 A3002 RMK AO2 SLP172 P0000 T00001061"
## [2] "KORD 010251Z 05011KT 10SM -RA BKN050 BKN070 OVC090 M01/M05 A3004 RMK AO2 DZE48RAB48 SLP178 60000 P0000 T10061050 51017"
## [3] "KORD 010351Z 06014G21KT 10SM -FZRA FEW017 SCT045 OVC055 M02/M05 A3003 RMK AO2 RAE11FZRAB39SNB11E39 SLP175 P0000 I1000 T10171050"
## [4] "KORD 010451Z 06015KT 10SM -FZRA BKN014 OVC019 M02/M05 A3001 RMK AO2 SLP169 FZRAE14B36 P0003 I1000 T10221050"
## [5] "KORD 010551Z 07013KT 10SM BKN014 OVC070 M02/M05 A3000 RMK AO2 FZRAE30PLB09E48 SLP166 P0000 60000 I1000 I6000 T10221050 10006 21022 401111022 56011"
##
## Mismatch at time 2016-03-24 16:51 UTC
## [1] "KORD 241951Z 01019KT 10SM BKN010 OVC015 03/02 A2948 RMK AO2 SLP987 T00330017 $"
## [2] "KORD 242051Z 34014G22KT 10SM OVC010 03/01 A2953 RMK AO2 PRESRR SLP004 T00280011 53009 $"
## [3] "KORD 242151Z 34014KT 1 3/4SM -RASN BR SCT010 OVC016 01/00 A2960 RMK AO2 SLP029 T00110000 $"
## [4] "KORD 242251Z 35011G19KT 5SM -SN BR SCT007 BKN019 OVC046 01/00 A2962 RMK AO2 RAE14 SLP037 T00110000 $"
## [5] "KORD 242351Z 34016G23KT 10SM FEW011 BKN018 OVC025 01/M02 A2968 RMK AO2 SLP055 SNE31 60012 T00061017 10039 20006 53025 $"
##
## Mismatch at time 2016-07-13 13:51 UTC
## [1] "KORD 131251Z 00000KT 10SM FEW055 FEW075 BKN250 25/21 A2990 RMK AO2 SLP118 T02500211"
## [2] "KORD 131351Z 18009KT 9SM -RA FEW070 BKN090 BKN250 26/22 A2991 RMK AO2 RAB51 SLP124 P0000 T02560217"
## [3] "KORD 131851Z 22009G18KT 10SM FEW027 SCT047 BKN250 28/22 A2984 RMK AO2 SLP099 T02780217"
## [4] "KORD 131951Z 23008KT 10SM BKN030 OVC070 29/22 A2981 RMK AO2 SLP089 T02890222"
## [5] "KORD 132051Z 21009KT 10SM SCT025 BKN033 BKN040 28/22 A2980 RMK AO2 SLP087 T02830222 56022"
##
## Mismatch at time 2016-07-13 14:51 UTC
## [1] "KORD 131351Z 18009KT 9SM -RA FEW070 BKN090 BKN250 26/22 A2991 RMK AO2 RAB51 SLP124 P0000 T02560217"
## [2] "KORD 131851Z 22009G18KT 10SM FEW027 SCT047 BKN250 28/22 A2984 RMK AO2 SLP099 T02780217"
## [3] "KORD 131951Z 23008KT 10SM BKN030 OVC070 29/22 A2981 RMK AO2 SLP089 T02890222"
## [4] "KORD 132051Z 21009KT 10SM SCT025 BKN033 BKN040 28/22 A2980 RMK AO2 SLP087 T02830222 56022"
## [5] "KORD 132151Z 20010KT 10SM -RA FEW060 SCT080 BKN210 OVC250 28/23 A2979 RMK AO2 RAB47 SLP081 P0000 T02830233"
##
## Mismatch at time 2016-07-13 15:51 UTC
## [1] "KORD 131851Z 22009G18KT 10SM FEW027 SCT047 BKN250 28/22 A2984 RMK AO2 SLP099 T02780217"
## [2] "KORD 131951Z 23008KT 10SM BKN030 OVC070 29/22 A2981 RMK AO2 SLP089 T02890222"
## [3] "KORD 132051Z 21009KT 10SM SCT025 BKN033 BKN040 28/22 A2980 RMK AO2 SLP087 T02830222 56022"
## [4] "KORD 132151Z 20010KT 10SM -RA FEW060 SCT080 BKN210 OVC250 28/23 A2979 RMK AO2 RAB47 SLP081 P0000 T02830233"
## [5] "KORD 132251Z 22008G17KT 10SM FEW060 FEW085 SCT210 OVC250 28/22 A2977 RMK AO2 RAE07 SLP077 P0000 T02780222"
##
## Mismatch at time 2016-07-13 17:51 UTC
## [1] "KORD 132051Z 21009KT 10SM SCT025 BKN033 BKN040 28/22 A2980 RMK AO2 SLP087 T02830222 56022"
## [2] "KORD 132151Z 20010KT 10SM -RA FEW060 SCT080 BKN210 OVC250 28/23 A2979 RMK AO2 RAB47 SLP081 P0000 T02830233"
## [3] "KORD 132251Z 22008G17KT 10SM FEW060 FEW085 SCT210 OVC250 28/22 A2977 RMK AO2 RAE07 SLP077 P0000 T02780222"
## [4] "KORD 132351Z 26010KT 10SM -TSRA FEW055 SCT085CB BKN160 OVC180 27/22 A2978 RMK AO2 RAB47 TSB35 SLP077 OCNL LTGIC VC W TS VC W MOV E P0000 60000 T02720222 10289 20272 55009"
## [5] "KORD 140051Z 16017G26KT 10SM -TSRA FEW043 SCT090CB BKN110 BKN160 22/21 A2972 RMK AO2 PK WND 13030/0035 SLP059 FRQ LTGIC VC E-SE TS VC E-SE MOV E P0004 T02170206"
##
## Mismatch at time 2016-08-29 14:51 UTC
## [1] "KORD 291751Z 09006KT 10SM SCT026 SCT048 BKN170 BKN250 29/21 A3023 RMK AO2 SLP233 T02940211 10294 20222 58003"
## [2] "KORD 291851Z 06006KT 10SM FEW025 BKN048 BKN170 BKN250 30/21 A3021 RMK AO2 SLP226 OCNL LTGIC DSNT SW CB DSNT SW T03000211 $"
## [3] "KORD 291951Z 25009KT 1SM R10L/1200VP6000FT +TSRA BKN041CB BKN055 OVC110 25/21 A3022 RMK AO2 SLP230 TSB11 CONS LTGICCG OHD TS OHD MOV E P0001 T02500211 $"
## [4] "KORD 292051Z 01005KT 9SM TS SCT055CB BKN110 OVC140 24/22 A3019 RMK AO2 RAE48 SLP217 OCNL LTGICCG VC NW-W-NE TS VC NW-N-NE MOV E P0039 60040 T02390222 58010 $"
## [5] "KORD 292151Z 02005KT 10SM FEW050 FEW075 SCT140 BKN180 OVC250 25/22 A3018 RMK AO2 RAB2059E16 TSE06 SLP215 P0000 T02500222 $"# Mismatches due to SN without SNB on 2016-02-09, 2016-03-24, 2016-04-08, 2016-12-23
tmp <- intervalConsistency(kordSnow2016, pType="SN")## intMETAR
## precipMETAR FALSE TRUE
## FALSE 8532 0
## TRUE 7 276
##
## Mismatch at time 2016-02-09 18:51 UTC
## [1] "KORD 092251Z 32012KT 10SM SCT032 OVC060 M08/M14 A2990 RMK AO2 SLP136 SNE30 P0000 T10831139"
## [2] "KORD 092351Z 31014KT 10SM SCT028 BKN036 OVC070 M09/M14 A2992 RMK AO2 SNB25E33 SLP144 60000 T10891144 11078 21089 53023"
## [3] "KORD 100051Z 31012KT 4SM -SN FEW024 OVC040 M09/M13 A2993 RMK AO2 SLP148 P0003 T10941133"
## [4] "KORD 100151Z 29011KT 5SM -SN OVC040 M09/M13 A2994 RMK AO2 SLP150 P0002 T10941133"
## [5] "KORD 100251Z 29011KT 3SM -SN SCT021 OVC031 M09/M12 A2994 RMK AO2 SNE16B36 SLP150 60000 T10941122 51006"
##
## Mismatch at time 2016-02-09 19:51 UTC
## [1] "KORD 092351Z 31014KT 10SM SCT028 BKN036 OVC070 M09/M14 A2992 RMK AO2 SNB25E33 SLP144 60000 T10891144 11078 21089 53023"
## [2] "KORD 100051Z 31012KT 4SM -SN FEW024 OVC040 M09/M13 A2993 RMK AO2 SLP148 P0003 T10941133"
## [3] "KORD 100151Z 29011KT 5SM -SN OVC040 M09/M13 A2994 RMK AO2 SLP150 P0002 T10941133"
## [4] "KORD 100251Z 29011KT 3SM -SN SCT021 OVC031 M09/M12 A2994 RMK AO2 SNE16B36 SLP150 60000 T10941122 51006"
## [5] "KORD 100351Z 31012KT 10SM BKN042 OVC085 M10/M14 A2994 RMK AO2 SNE38 SLP149 P0002 T11001139"
##
## Mismatch at time 2016-03-24 16:51 UTC
## [1] "KORD 241951Z 01019KT 10SM BKN010 OVC015 03/02 A2948 RMK AO2 SLP987 T00330017 $"
## [2] "KORD 242051Z 34014G22KT 10SM OVC010 03/01 A2953 RMK AO2 PRESRR SLP004 T00280011 53009 $"
## [3] "KORD 242151Z 34014KT 1 3/4SM -RASN BR SCT010 OVC016 01/00 A2960 RMK AO2 SLP029 T00110000 $"
## [4] "KORD 242251Z 35011G19KT 5SM -SN BR SCT007 BKN019 OVC046 01/00 A2962 RMK AO2 RAE14 SLP037 T00110000 $"
## [5] "KORD 242351Z 34016G23KT 10SM FEW011 BKN018 OVC025 01/M02 A2968 RMK AO2 SLP055 SNE31 60012 T00061017 10039 20006 53025 $"
##
## Mismatch at time 2016-03-24 17:51 UTC
## [1] "KORD 242051Z 34014G22KT 10SM OVC010 03/01 A2953 RMK AO2 PRESRR SLP004 T00280011 53009 $"
## [2] "KORD 242151Z 34014KT 1 3/4SM -RASN BR SCT010 OVC016 01/00 A2960 RMK AO2 SLP029 T00110000 $"
## [3] "KORD 242251Z 35011G19KT 5SM -SN BR SCT007 BKN019 OVC046 01/00 A2962 RMK AO2 RAE14 SLP037 T00110000 $"
## [4] "KORD 242351Z 34016G23KT 10SM FEW011 BKN018 OVC025 01/M02 A2968 RMK AO2 SLP055 SNE31 60012 T00061017 10039 20006 53025 $"
## [5] "KORD 250051Z 35016G21KT 10SM SCT017 OVC025 00/M02 A2971 RMK AO2 PK WND 36029/0031 SLP066 T00001022 $"
##
## Mismatch at time 2016-04-08 17:51 UTC
## [1] "KORD 082051Z 29018G24KT 10SM FEW046 SCT055 OVC090 04/M03 A2981 RMK AO2 PK WND 30035/2025 SNE03B28E40 SLP098 P0000 60000 T00441033 50002"
## [2] "KORD 082151Z 32016G25KT 9SM BKN034 BKN046 OVC055 03/M03 A2982 RMK AO2 PK WND 32030/2057 SNB29E46 SLP103 P0000 T00281033"
## [3] "KORD 082251Z 31014G31KT 9SM -SN SCT045 BKN050 OVC080 02/M06 A2983 RMK AO2 PK WND 32031/2243 SLP108 P0000 T00221061"
## [4] "KORD 082351Z 31014G25KT 10SM SCT060 SCT080 03/M09 A2984 RMK AO2 SNE00 SLP112 P0000 60000 T00281089 10050 20017 52012"
## [5] "KORD 090051Z 35012G21KT 10SM FEW060 SCT080 01/M07 A2987 RMK AO2 PK WND 32030/2355 SLP121 T00111072"
##
## Mismatch at time 2016-12-23 19:51 UTC
## [1] "KORD 232351Z 14008KT 6SM BR SCT037 OVC055 01/M01 A2996 RMK AO2 RAE01B27E32SNB01E48 SLP152 4/003 P0003 60006 T00111006 10017 20011 56024"
## [2] "KORD 240051Z 20007KT 6SM BR FEW013 OVC055 01/M01 A2997 RMK AO2 SLP153 T00111006"
## [3] "KORD 240151Z 17011KT 4SM -SN BR SCT015 OVC039 01/M01 A2993 RMK AO2 SLP143 P0000 T00111006"
## [4] "KORD 240251Z 20007KT 2SM -SN BR OVC008 01/00 A2994 RMK AO2 SLP143 P0000 60000 T00110000 58008"
## [5] "KORD 240351Z 21008KT 2 1/2SM -SN BR OVC006 01/00 A2992 RMK AO2 RAB09E43SNE09B43 SLP138 P0004 T00060000"
##
## Mismatch at time 2016-12-23 20:51 UTC
## [1] "KORD 240051Z 20007KT 6SM BR FEW013 OVC055 01/M01 A2997 RMK AO2 SLP153 T00111006"
## [2] "KORD 240151Z 17011KT 4SM -SN BR SCT015 OVC039 01/M01 A2993 RMK AO2 SLP143 P0000 T00111006"
## [3] "KORD 240251Z 20007KT 2SM -SN BR OVC008 01/00 A2994 RMK AO2 SLP143 P0000 60000 T00110000 58008"
## [4] "KORD 240351Z 21008KT 2 1/2SM -SN BR OVC006 01/00 A2992 RMK AO2 RAB09E43SNE09B43 SLP138 P0004 T00060000"
## [5] "KORD 240451Z 23007KT 2SM -DZ BR OVC006 01/00 A2992 RMK AO2 DZB20SNE10 SLP139 P0000 T00110000"Example #31: All Precipitation Types
The METAR data can be explored to find all the precipitation types it contains. Broadly, a precipitation type should meet five criteria:
- Occurs prior to RMK (the remarks section has letter-only data that is not precipitation)
- Preceded by a space
- Optionally, begins with + (heavy) or - (light)
- Contains only capital letters (no numbers) after the optional +/-
- Trailed by a space
This will extract some non-precipitation types such as CLR and AUTO, so some iteration is expected.
Example code includes:
# Function to find precipitation types
findPrecipTypes <- function(lst, precipRegex="(?<= )[+-]?[A-Z]+(?= )", priorTo="RMK", exclTypes=NULL) {
metar <- lst[["testFileProc"]][["origMETAR"]]
# Keep only everything prior to priorTo
if (!is.null(priorTo)) {
rmkLoc <- str_locate(metar, priorTo)
rmkLoc[is.na(rmkLoc)] <- -1 # keep everything if NA
metar <- str_sub(metar, start=1, end=rmkLoc[, 1]-1)
}
# Exclude any items from the METAR
if (!is.null(exclTypes)) {
for (excl in exclTypes) {
metar <- str_replace(metar, excl, "")
}
}
# Is there a possible precipitation type in the file?
pExists <- grepl(precipRegex, metar, perl=TRUE)
cat("\nPrecipitation data status by METAR record\n\n")
print(table(pExists))
cat("\n")
# Find the precipitation matches
precipMatches <- str_match_all(metar, pattern=precipRegex)
# Confirm that there are a maximum of two precipitation types per METAR
listLengths <- sapply(precipMatches, FUN=length)
if (max(listLengths) > 2) {
stop("Hard-coded for at most 2 precipitation matches, please investigate")
} else if (max(listLengths)==0) {
cat("\nNo precipitation detected in this file\n")
}
if (max(listLengths)==2) {
cat("\nMultiple Precipitation types in the same record include\n")
sapply(precipMatches[listLengths==2], FUN=paste, collapse=" ") %>%
table() %>%
sort(decreasing=TRUE) %>%
print()
}
if (sum(listLengths==1) > 0) {
cat("\nSingle Precipitation types in the same record include\n")
sapply(precipMatches[listLengths==1], FUN=c) %>%
table() %>%
sort(decreasing=TRUE) %>%
print()
}
# Extract the second column and summarize precipitation types
# table(precipMatches[, 2]) %>% sort(decreasing=TRUE) %>% print()
precipMatches
}
# Original pass for Chicago, IL
x1 <- findPrecipTypes(kordRain2016)##
## Precipitation data status by METAR record
##
## pExists
## FALSE TRUE
## 6792 2023
##
##
## Multiple Precipitation types in the same record include
## .
## -RA BR -SN BR -DZ BR RA BR TSRA BR +TSRA BR +RA BR
## 151 108 47 34 11 10 7
## SN FG BR CLR SN FZFG -FZDZSN BR -TSRA BR -DZ FG -FZDZ BR
## 7 6 6 4 3 2 2
## -RASN BR -PL BR -RAPL BR -SNPL BR +SN FG +SN FZFG BR BCFG
## 2 1 1 1 1 1 1
## DZ BR HZ CLR MIFG BR TS BR VCTS -RA VCTS BR
## 1 1 1 1 1 1
##
## Single Precipitation types in the same record include
## .
## CLR BR -RA -SN -TSRA FG HZ -DZ TS +TSRA
## 805 327 210 150 26 25 24 13 9 3
## RA TSRA VCTS -FZRA AUTO FZFG -FZRAPL -RASN -SNPL SN
## 3 3 3 2 2 2 1 1 1 1# Exclude AUTO and CLR as they are a record state and cloud type, not precipitation
x2 <- findPrecipTypes(kordRain2016, exclTypes=c("CLR", "AUTO"))##
## Precipitation data status by METAR record
##
## pExists
## FALSE TRUE
## 7599 1216
##
##
## Multiple Precipitation types in the same record include
## .
## -RA BR -SN BR -DZ BR RA BR TSRA BR +TSRA BR +RA BR
## 151 108 47 34 11 10 7
## SN FG SN FZFG -FZDZSN BR -TSRA BR -DZ FG -FZDZ BR -RASN BR
## 7 6 4 3 2 2 2
## -PL BR -RAPL BR -SNPL BR +SN FG +SN FZFG BR BCFG DZ BR
## 1 1 1 1 1 1 1
## MIFG BR TS BR VCTS -RA VCTS BR
## 1 1 1 1
##
## Single Precipitation types in the same record include
## .
## BR -RA -SN -TSRA FG HZ -DZ TS +TSRA RA
## 333 210 150 26 25 25 13 9 3 3
## TSRA VCTS -FZRA FZFG -FZRAPL -RASN -SNPL SN
## 3 3 2 2 1 1 1 1# Original pass for Las Vegas, NV
x3 <- findPrecipTypes(klasRain2016)##
## Precipitation data status by METAR record
##
## pExists
## FALSE TRUE
## 6205 2613
##
##
## Multiple Precipitation types in the same record include
## .
## AUTO CLR VCBLDU CLR -RA BR RA BR -TSRA BR -RA VCFG +RA BR
## 7 7 5 3 2 1 1
## FU HZ HZ BLDU VCTS RA
## 1 1 1
##
## Single Precipitation types in the same record include
## .
## CLR -RA VCBLDU -TSRA TS HZ VCFG BR AUTO RA VCTS
## 2393 72 54 17 13 8 8 4 3 3 3
## DU TSRA +TSRA BLDU
## 2 2 1 1# Exclude AUTO and CLR as they are a record state and cloud type, not precipitation
x4 <- findPrecipTypes(klasRain2016, exclTypes=c("CLR", "AUTO"))##
## Precipitation data status by METAR record
##
## pExists
## FALSE TRUE
## 8608 210
##
##
## Multiple Precipitation types in the same record include
## .
## -RA BR RA BR -TSRA BR -RA VCFG +RA BR FU HZ HZ BLDU VCTS RA
## 5 3 2 1 1 1 1 1
##
## Single Precipitation types in the same record include
## .
## -RA VCBLDU -TSRA TS HZ VCFG BR RA VCTS DU TSRA
## 72 61 17 13 8 8 4 3 3 2 2
## +TSRA BLDU
## 1 1